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Cloud Data Lakes And Data Warehouse Consulting | Solve: Insights For Your Path Forward

Database vs Data Warehouse vs Data Lake | What is the Difference?

Choose Your Service Option

Data warehouse implementation / migration / optimization consulting

We offer advisory support or complete project management to help you:

  • Implement a cost-effective DWH solution under set time and budget.
  • Migrate your legacy DWH solution to the cloud to achieve dynamic scaling of the DWH infrastructure and optimize DWH performance and costs.
  • Upgrade the existing DWH solution to meet new business needs (e.g., add real-time analytics).

End-to-end data warehouse implementation

We help you:

  • Consolidate disjointed data sources into centralized storage.
  • Ensure uninterrupted data flow via planning and implementing the required integrations with other systems (e.g., with an enterprise data lake).
  • Achieve high data quality.
  • Ensure data security and compliance.

Data warehouse support and evolution

We help you meet newly arising analytics needs by:

  • Reducing data latency.
  • Solving performance and concurrency problems.
  • Lowering storage and processing costs.
  • Achieving DWH stability.
  • Ensuring timely and quality data flow for business users with near-zero DWH downtime.
  • Providing complimentary services (e.g., AI/ML services, data lake consulting, BI and visualization services).

What is a data warehouse?

A data warehouse is a central repository of information that can be analyzed to make more informed decisions. Data flows into a data warehouse from transactional systems, relational databases, and other sources, typically on a regular cadence. Business analysts, data engineers, data scientists, and decision makers access the data through business intelligence (BI) tools, SQL clients, and other analytics applications.

Data and analytics have become indispensable to businesses to stay competitive. Business users rely on reports, dashboards, and analytics tools to extract insights from their data, monitor business performance, and support decision making. Data warehouses power these reports, dashboards, and analytics tools by storing data efficiently to minimize the input and output (I/O) of data and deliver query results quickly to hundreds and thousands of users concurrently.

Database vs Data Warehouse vs Data Lake | What is the Difference?
Database vs Data Warehouse vs Data Lake | What is the Difference?

The Modern Data Warehouse

Our company has a history of architecting, implementing, and loading data warehouses, which has enabled us to build data warehouse consulting expertise based on the following principles:

  • Data Collection – creating solutions that source data located both inside and outside an organization
  • Data Organization – organizing data in a manner that is conducive to obtaining powerful analysis and positive business actions
  • Perform Deep Queries – creating solutions that empower end-users to perform deep queries quickly and accurately
  • Data Retrieval – implementing products that enable the retrieval of data at lightning speed and the sharing of it seamlessly across an organization

These principles have served us well in our endeavors over the years, and they have provided a foundation that has allowed us to transition into creating modern, cloud-based architectures for clients. These architectures are crafted around data lakes and lakehouses, with a focus on delivering powerful insights efficiently and effectively.

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What is a data lake?

The sheer number of data sources in a modern enterprise environment, combined with the challenges of storing, processing, and accessing both structured and semi-structured data, has driven demand for sophisticated data warehouse solutions.

A data lake is a centralized repository that allows you to store all your raw data, structured and unstructured data at any scale. It can store data in its native format and process any variety of it, ignoring data storage limits.

Companies today are also starting to look at the value of data lakes. An Aberdeen survey saw organizations that implemented a data lake outperforming similar companies by 9% in organic revenue growth.

With the data lake solution, business users are gaining a deeper understanding of business situations as they have more context than ever before, allowing them to accelerate analytics experiments.

What are the cloud data lake platforms?

A cloud data lake is a cloud-hosted centralized repository that allows you to store all your structured and unstructured data at any scale. Data lakes are usually considered complementary solutions to data warehouses.

The most popular cloud providers, Amazon, Google, and Microsoft, all offer cloud data lakes and data warehouses:

Amazon Web Services

AWS Lake Formation allows you to create a secure data lake in days. In a data lake, all your data is centralized, curated, and ready for analysis. Amazon Redshift allows you to run complex analytic queries against petabytes of enterprise data. And with Amazon QuickSight, you can create stunning visualizations and rich dashboards that can be accessed from any browser or mobile device. AWS Glue service can be used to perform data transformation. AWS Athena can be used to analyze data stored in AWS S3.

Google Cloud Services

Google Cloud Storage (GCS) is a lower-cost cloud data lake. On top of that, the Google BigQuery solution offers an enterprise data warehouse for analytics. The serverless solution creates a logical data warehouse from managed columnar storage, object storage, and spreadsheets. BigQuery uses streaming ingestion to capture data in real-time and runs on the Google Cloud Platform. Users can also share data, queries, spreadsheets, and reports.

Microsoft Azure Cloud

Azure Data Lake Store (ADLS), is a hyper-scale repository for an enterprise data lake. It enables developers, data scientists, and analysts to store, process, and analyze data of any size, shape, or speed across platforms and languages. In addition, it integrates with operational stores and data warehouses.

Snowflake Cloud Data Platform

Snowflake works on all of the above cloud platforms. The solution loads raw data from JSON, Avro, and XML sources. Snowflake supports updates, deletes, analytical functions, transactions, and complex joins. It requires no infrastructure or management. The columnar database engine crunches data, processes reports, and runs analytics.

What are the benefits of building data lakes in the cloud?

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Our DevOps consulting services support the DevOps cultural change in your organization along with the DevOps tools as you progress towards DevOps principles and software development excellence.

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Data Lakes & Warehouses

Implementing, configuring or upgrading an enterprise data warehouse is one of the most important projects your business will embark on.

The modern business generates and captures an incredible amount of data – often to the point of overwhelming administrators and legacy databases.

The sheer number of data sources in today’s enterprise environment, combined with the challenges of storing, processing, and accessing both structured and unstructured data, has driven demand for sophisticated data warehouse solutions.

OneSix’s data warehouse consulting services can help address any data-related concern, whether you need to set up a centralized repository that integrates with other platforms or you want to optimize your existing system to better support analytics initiatives. Our experienced team of data warehouse professionals has seen and done it all.

What is a Data Warehouse?
What is a Data Warehouse?

Data lakedata warehouse

Now you know what a data lake is, why it matters, and how it’s used across a variety of organizations. But what’s the difference between a data lake and a data warehouse? And when is it appropriate to use one over the other?

While data lakes and data warehouses are similar in that they both store and process data, each have their own specialties, and therefore their own use cases. That’s why it’s common for an enterprise-level organization to include a data lake and a data warehouse in their analytics ecosystem. Both repositories work together to form a secure, end-to-end system for storage, processing, and faster time to insight.

A data lake captures both relational and non-relational data from a variety of sources—business applications, mobile apps, IoT devices, social media, or streaming—without having to define the structure or schema of the data until it is read. Schema-on-read ensures that any type of data can be stored in its raw form. As a result, data lakes can hold a wide variety of data types, from structured to semi-structured to unstructured, at any scale. Their flexible and scalable nature make them essential for performing complex forms of data analysis using different types of compute processing tools like Apache Spark or Azure Machine Learning.

By contrast, a data warehouse is relational in nature. The structure or schema is modeled or predefined by business and product requirements that are curated, conformed, and optimized for SQL query operations. While a data lake holds data of all structure types, including raw and unprocessed data, a data warehouse stores data that has been treated and transformed with a specific purpose in mind, which can then be used to source analytic or operational reporting. This makes data warehouses ideal for producing more standardized forms of BI analysis, or for serving a business use case that has already been defined.

Data lake Data warehouse
Type Structured, semi-structured, unstructured Structured
Relational, non-relational Relational
Schema Schema on read Schema on write
Format Raw, unfiltered Processed, vetted
Sources Big data, IoT, social media, streaming data Application, business, transactional data, batch reporting
Scalability Easy to scale at a low cost Difficult and expensive to scale
Users Data scientists, data engineers Data warehouse professionals, business analysts
Use cases Machine learning, predictive analytics, real-time analytics Core reporting, BI

Data lake definition

This introductory guide explores the many benefits and use cases of a data lake. Learn what a data lake is, why it matters, and discover the difference between data lakes and data warehouses. But first, let’s define data lake as a term.

A data lake is a centralized repository that ingests and stores large volumes of data in its original form. The data can then be processed and used as a basis for a variety of analytic needs. Due to its open, scalable architecture, a data lake can accommodate all types of data from any source, from structured (database tables, Excel sheets) to semi-structured (XML files, webpages) to unstructured (images, audio files, tweets), all without sacrificing fidelity. The data files are typically stored in staged zones—raw, cleansed, and curated—so that different types of users may use the data in its various forms to meet their needs. Data lakes provide core data consistency across a variety of applications, powering big data analytics, machine learning, predictive analytics, and other forms of intelligent action.

Lộ diện Data Lake, Data Warehouse và ví dụ thực tế với Hadoop, Hive, Spark
Lộ diện Data Lake, Data Warehouse và ví dụ thực tế với Hadoop, Hive, Spark

Choosing a Data LakeData Warehouse – Which is Right?

A recent Gartner study found that 57% of IT data and analytics leaders are using data warehouses, while 39% were using data lakes. Should your business choose one over the other, or some combination of both?

When you’re debating between data lakes and data warehouses, there’s honestly no “best option.” Really, the right storage solution is going to be what suits your objectives, budget, and skillsets. Our team at TierPoint can help with selection, implementation, and management – learn about our Data and Analytics Consulting Services.

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  • Prioritize time-sensitive jobs and schedule batch processes when it makes sense for you.
  • Run batch systems on or offline, with minimal user interaction and reduced opportunities for error.
Building data lakes on Google Cloud
Building data lakes on Google Cloud

Similarities between data warehouses, data marts, and data lakes

Organizations today have access to ever-increasing volumes of data. However, they must sort, process, filter, and analyze the raw data to derive practical benefits. At the same time, they also have to follow rigid data protection and security practices for regulatory compliance. For example, here are practices organizations must follow:

  • Collect data from different sources like applications, vendors, Internet of Things (IoT) sensors, and other third parties.
  • Process data into a consistent, trustworthy, and useful format. For example, organizations could process data to make sure all dates in the system are in a common format or summarize daily reports.
  • Prepare the data by formatting XML files for machine learning software or generating reports for humans.

Organizations use various tools and solutions to achieve their data analytics outcomes. Data warehouses, marts, and lakes are all solutions that help with storing data.

Benefits of a cloud-based data warehouse, data lake, and data mart

All three storage solutions help you increase your data’s availability, reliability, and security. Here are examples of how you can use them:

  • Store your business data securely for analytics
  • Store unlimited data volume for as long as you need it
  • Break down silos with data integration from multiple business processes
  • Analyze historical data or legacy databases
  • Undertake real-time and batch data analysis

In addition, all three solutions are cost-efficient—you only pay for the storage space that you use. You can store all your data, analyze it for patterns and trends, and use the information to optimize your business operations.

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What is a Data Lake?
What is a Data Lake?

Solve: Insights for your path forward

4 Strategies to Avoid the Data Swamp

Organizations are increasingly eager to monetize their mountains of data. There are many ways to make that happen.

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When it comes to hiring data warehouse service providers for design and implementation, ExistBI is technology agnostic and vendor neutral, which means we always put your interests first and develop the most appropriate solution based on your unique requirements. Our specialists can join your business stakeholders and implementation team at every phase of the journey offering subject matter, industry and technology specific Data Warehouse consulting.

ExistBI’s certified data warehouse consulting team also assist clients transition from on-premise to a hybrid or Cloud data warehouse approach using some of the leading platforms such as: Microsoft SQL Data Warehouse, Azure SQL Data Warehouse Azure Synapse, Azure Data Lake, AWS Redshift, S3/Lake Formation, Snowflake, Oracle, SAP Analytics Cloud, Google Big Query Cloud, Cloudera Data Platform, Databricks Data Lakehouse just to name a few.

Our data warehouse consultants along with our highly experienced strategy team help customers with their digital transformation through providing analytics insights, predictive analytics, on-premise or cloud data warehousing or data lakes, data migration, data integration, data governance, data quality, master data management and data security initiatives.

ExistBI is recognized as one of the worlds only data warehouse consulting companies that can safely guide you from a Phase 1 data warehouse assessment through to Phase 4 data warehouse support.

Databases Vs Data Warehouses Vs Data Lakes - What Is The Difference And Why Should You Care?
Databases Vs Data Warehouses Vs Data Lakes – What Is The Difference And Why Should You Care?

How is a data warehouse architected?

A data warehouse architecture is made up of tiers. The top tier is the front-end client that presents results through reporting, analysis, and data mining tools. The middle tier consists of the analytics engine that is used to access and analyze the data. The bottom tier of the architecture is the database server, where data is loaded and stored. Data is stored in two different types of ways: 1) data that is accessed frequently is stored in very fast storage (like SSD drives) and 2) data that is infrequently accessed is stored in a cheap object store, like Amazon S3. The data warehouse will automatically make sure that frequently accessed data is moved into the “fast” storage so query speed is optimized.

Data lakedata lakehouse

Now you know the difference between a data lake vs. a data warehouse. But what’s the difference between a data lake and a data lakehouse? And is it necessary to have both?

Despite its many advantages, a traditional data lake is not without its drawbacks. Because data lakes can accommodate all types of data from all kinds of sources, issues related to quality control, data corruption, and improper partitioning can occur. A poorly managed data lake not only tarnishes data integrity, but it can also lead to bottlenecks, slow performance, and security risks.

That’s where the data lakehouse comes into play. A data lakehouse is an open standards-based storage solution that is multifaceted in nature. It can address the needs of data scientists and engineers who conduct deep data analysis and processing, as well as the needs of traditional data warehouse professionals who curate and publish data for business intelligence and reporting purposes. The beauty of the lakehouse is that each workload can seamlessly operate on top of the data lake without having to duplicate the data into another structurally predefined database. This ensures that everyone is working on the most up-to-date data, while also reducing redundancies.

Data lakehouses address the challenges of traditional data lakes by adding a Delta Lake storage layer directly on top of the cloud data lake. The storage layer provides a flexible analytic architecture that can handle ACID (atomicity, consistency, isolation, and durability) transactions for data reliability, streaming integrations, and advanced features like data versioning and schema enforcement. This allows for a range of analytic activity over the lake, all without compromising core data consistency. While the necessity of a lakehouse depends on how complex your needs are, its flexibility and range make it an optimal solution for many enterprise orgs.

Data lake Data lakehouse
Type Structured, semi-structured, unstructured Structured, semi-structured, unstructured
Relational, non-relational Relational, non-relational
Schema Schema on read Schema on read, schema on write
Format Raw, unfiltered, processed, curated Raw, unfiltered, processed, curated, delta format files
Sources Big data, IoT, social media, streaming data Big data, IoT, social media, streaming data, application, business, transactional data, batch reporting
Scalability Easy to scale at a low cost Easy to scale at a low cost
Users Data scientists Business analysts, data engineers, data scientists
Use cases Machine learning, predictive analytics Core reporting, BI, machine learning, predictive analytics
Ủa Database, Datalake, Data Warehouse là gì ? Giải thích bởi Senior Data Engineer
Ủa Database, Datalake, Data Warehouse là gì ? Giải thích bởi Senior Data Engineer

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In the era of data-driven decision-making, choosing the right data storage solution is crucial for organizations. Although two prominent options – data lakes vs. data warehouses – may sound like they’re describing the same thing, they offer distinct approaches to data management. As with any decision, the choice between data lakes and data warehouses comes with trade-offs.

In this blog, we’ll cover the differences between a data lake and a data warehouse, the benefits and disadvantages of each, and the scenarios that call for which solutions.

Data Lakes

Data lakes are central repositories for collecting and storing large amounts of data. This data can range from structured to semi-structured to unstructured. They often contain many different data types.

A key concept of a data lake is that its entities are “schema-on-read.” This requires data to be transformed at the time of analysis (or read) versus the data ingestion process. It gives data engineers wiggle room to customize the way the data is organized, and the schema can be shifted to allow for exploration and analysis by the engineers as well as data scientists.

Tools such as Power BI and Fabric can attach directly to data lakes for deeper analysis. Additionally, our company is well-versed in constructing data lakes in Azure Data Lake Storage (ADLS) Gen2. We use these repositories as the basis for building lakehouses.

Tại sao mình nghỉ việc Data Scientist? Lời khuyên cho các bạn muốn làm Data Scientist.
Tại sao mình nghỉ việc Data Scientist? Lời khuyên cho các bạn muốn làm Data Scientist.

What’s the Difference Between a Data Warehouse, Data Lake, and Data Mart?

Data warehouses, data lakes, and data marts are different cloud storage solutions. A data warehouse stores data in a structured format. It is a central repository of preprocessed data for analytics and business intelligence. A data mart is a data warehouse that serves the needs of a specific business unit, like a company’s finance, marketing, or sales department. On the other hand, a data lake is a central repository for raw data and unstructured data. You can store data first and process it later on.

Your data warehouse is more than just a point of storage for your organization’s data.

It powers advanced analytics by compiling data from every available source and generating valuable reports that drive strategic decision-making. Any obstacle that gets in the way of those goals is holding your business back.

Ask yourself these questions if you think you might be ready for a data warehouse upgrade:

What are the core aspects of data analytics?

Every company or organization has a huge repository of information to pull from to fuel their data analytics projects, but getting started takes a good amount of preparation. There are several components to a successful analytics strategy.

Is your data warehouse scalable?

It can be extremely difficult to scale up storage and resourcing needs with traditional on-prem data warehouses since they are limited by on-site architecture and equipment. Cloud-based solutions enable on-demand scalability to support even the most complex analytics projects.

Is it cost-effective?

On-prem data warehouses can be expensive to maintain and expand to account for growing data demands. Hosted data warehouses lower your total cost of ownership while providing more flexibility and better performance.

Does it support end-to-end integration?

Fueling your advanced analytics with as much relevant data as possible will ensure you get the best and most insightful results possible. Data silos will only stand in the way of your analytics efforts.

Can it support sophisticated analytics?

Realizing the full potential of big data means being able to leverage real-time data to guide your business decisions. If your data warehouse can’t support this level of data management and analytics on a day-to-day basis, it may be time for an upgrade.

What can OneSix do to help?

Data warehouse consulting services are absolutely essential for any business that wants to take advantage of the latest big data and advanced analytics capabilities. The demands of today’s data storage, reporting, and analytics can fluctuate significantly depending on the project and both the volume and type of data used. Capturing real-time data and extracting actionable insights is incredibly resource-intensive, but it’s not cost-effective to resource for your most extreme requirements.

OneSix understands the intricacies of enterprise data warehouse management and how to get the best solution with the lowest cost of ownership. Cloud-based data warehousing often presents the most ideal option available since storage and compute resources can be scaled up or down as needed.

The latest and most advanced data warehouse solutions feature a multi-cluster architecture, allowing organizations to increase platform performance to account for more intensive query processing demands without affecting other workloads.

How can you choose the right data lakes and warehouses partner?

Trust only the best and most qualified experts configuring or upgrading an enterprise data warehouse.

OneSix data warehouse consulting services guide you through every phase of the project, from initial planning to integration and beyond. Working with a consulting company with OneSix’s knowledge, experience and skill insulates you against potential pitfalls and ensures the best results possible.

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Snowflake vs Databricks - The Ultimate Comparison 💡 Which One is Right for You?
Snowflake vs Databricks – The Ultimate Comparison 💡 Which One is Right for You?

What is data lake architecture?

At its core, a data lake is a storage repository with no set architecture of its own. In order to make the most of its capabilities, it requires a wide range of tools, technologies, and compute engines that help optimize the integration, storage, and processing of data. These tools work together to create a cohesively layered architecture, one that is informed by big data and runs on top of the data lake. This architecture may also form the operating structure of a data lakehouse. Every organization has its own unique configuration, but most data lakehouse architectures feature the following:

  • Resource management and orchestration. A resource manager enables the data lake to consistently execute tasks by allocating the right amount of data, resources, and computing power to the right places.
  • Connectors for easy access. A variety of workflows allow users to easily access—and share—the data they need in the form that they need it in.
  • Reliable analytics. A good analytics service should be fast, scalable, and distributed. It should also support a diverse range of workload categories across multiple languages.
  • Data classification. Data profiling, cataloging, and archiving help organizations keep track of data content, quality, location, and history.
  • Extract, load, transform (ELT) processes. ELT refers to the processes by which data is extracted from multiple sources and loaded into the data lake’s raw zone, then cleaned and transformed after extraction so that applications may readily use it.
  • Security and support. Data protection tools like masking, auditing, encryption, and access monitoring ensure that your data remains safe and private.
  • Governance and stewardship. For the data lake platform to run as smoothly as possible, users should be educated on its architectural configuration, as well as best practices for data and operations management.

Our Data Warehouse Consulting Service Approach

Our typical EDW project is divided in four main phases and focuses on using the right technology and processes effectively to achieve your business objectives:

Expert Data Warehouse Consulting Services

An excellent data warehouse is vital to data analytics success. We can help you architect and develop a new data warehouse or optimize an existing one.

What is Snowflake ? snowflake - concept, architecture, user workflow explained (2022)
What is Snowflake ? snowflake – concept, architecture, user workflow explained (2022)

Data lake use cases

With a well-architected solution, the potential for innovation is endless. Here are just a few examples of how organizations across a range of industries use data lake platforms to optimize their growth:

  • Streaming media. Subscription-based streaming companies collect and process insights on customer behavior, which they may use to improve their recommendation algorithm.
  • Finance. Investment firms use the most up-to-date market data, which is collected and stored in real time, to efficiently manage portfolio risks.
  • Healthcare. Healthcare organizations rely on big data to improve the quality of care for patients. Hospitals use vast amounts of historical data to streamline patient pathways, resulting in better outcomes and reduced cost of care.
  • Omnichannel retailer. Retailers use data lakes to capture and consolidate data that’s coming in from multiple touchpoints, including mobile, social, chat, word-of-mouth, and in person.
  • IoT. Hardware sensors generate enormous amounts of semi-structured to unstructured data on the surrounding physical world. Data lakes provide a central repository for this information to live in for future analysis.
  • Digital supply chain. Data lakes help manufacturers consolidate disparate warehousing data, including EDI systems, XML, and JSONs.
  • Sales. Data scientists and sales engineers often build predictive models to help determine customer behavior and reduce overall churn.

How does a data warehouse work?

A data warehouse may contain multiple databases. Within each database, data is organized into tables and columns. Within each column, you can define a description of the data, such as integer, data field, or string. Tables can be organized inside of schemas, which you can think of as folders. When data is ingested, it is stored in various tables described by the schema. Query tools use the schema to determine which data tables to access and analyze.

Why a Data Lakehouse Architecture
Why a Data Lakehouse Architecture

What Are the Key Differences Between a Data Lake and a Data Warehouse?

Although data lakes and data warehouses can both serve as cloud-based solutions, they differ in many ways, including: structure and design, purpose and focus, and the sources included.

Data Structure and Design

As previously mentioned, data is stored in its raw format in data lakes. This could be structured (like database tables or Excel sheets), unstructured (such as images or audio files), or semi-structured data (XML files, web pages, etc.). Structured data is stored in data warehouses, which are more ready for specific analytics and BI processes.

Purpose and Focus

The data structure and design of data lakes and data warehouses also dictate their respective purposes. Data lakes are well-suited for data exploration and discovery. They are often used in conjunction with machine learning or advanced analytics processes. On the other hand, data warehouses are primarily used more for reporting and decision-making instead of purely exploration.

Utilization and Users

Engineers and data scientists often prefer data lakes because of their flexibility with raw data. Data lakes enable users to access raw data for tasks like machine learning or initial exploration, with the option to structure and analyze it later. Conversely, data warehouses are primarily used by BI analysts and other users focused on creating front-end data reports. Data warehouses offer structured and organized data, making them suitable for users requiring refined and processed data for analysis and reporting.

Accessibility

While data lakes can be more accessible because of how adaptable the data can be in its raw format, this also means that an intermediate step may be required before it can be used to make connections and decisions. Data warehouses can be more accessible to business users, especially those who have experience with BI tools and how to analyze and build queries.

Data Sources

While data warehouses store structured data, data lakes can store data from a broader range of sources, including:

  • Internal and external databases
  • On-premises storage
  • Cloud storage
  • Sensor data
  • Internet of Things (IoT) devices
  • Log files
  • Unstructured data (i.e. videos, images, text)

Preprocessing

Data lakes store raw data in its native format, without needing preprocessing. Data warehouses, on the other hand, require preprocessing before data is loaded. For structured data, cleaning, transformation, and formatting are necessary to align it with a predefined schema before loading it into a data warehouse. This preprocessing guarantees data consistency and accuracy in the warehouse, enabling efficient querying and analysis using BI tools.

Data Quality

Because data lakes can store any kind of data, from structured to unstructured, it’s also safe to say that there is a lot of variability in quality. While high-quality data may exist in a lake, it can be harder to find.

Data warehouses, because they only store processed data, ensure that you can find high-quality data that’s ready for use.

Performance

It can be a lot harder to find what you’re looking for in a messy room compared to one that is organized. The same principle is true for data lakes and data warehouses. Think of a data lake like a messy room. Even if everything is present, and then some, finding the data you need through querying can take a while, which means performance suffers. Data warehouses can be queried more quickly, boosting performance.

Cost

Storage and processing demands are higher for data lakes because of their structureless nature. Managing data warehouses is less expensive, but they can require more upfront costs to set up in the first place.

Security

Data lakes don’t just contain a mix of structured and unstructured data. They also contain data with various levels of sensitivity. Because pre-processed data resides in a data lake, sensitive data may not have even been identified yet. Data warehouses tend to have more robust security features in place. These can include encryption, auditing, and access control.

How can AWS help with your data storage needs?

AWS provides the broadest selection of analytics services that fit all your data analytics needs. We enable industries and organizations of all sizes to reinvent their business with data. Here are examples of how you can use AWS:

  • Use Amazon Redshift for your data warehousing and data mart requirements. Get integrated insights by running real-time and predictive analytics on complex, scaled data across your operational databases, data lake, data warehouse, and thousands of third-party datasets. You can automatically create, train, and deploy machine learning models with ease.
  • Use AWS Lake Formation to build, manage, and secure a data lake within days. Quickly import data from all your data sources, then describe and manage them in a centralized data catalog.
  • Use Amazon S3 to build a custom data lake for big data analytics, artificial intelligence, machine learning, and high-performance computing applications.

Get started with data storage on AWS by creating a free account today.

What is Databricks? The Data Lakehouse You've Never Heard Of
What is Databricks? The Data Lakehouse You’ve Never Heard Of

What is a Data LakeData Warehouse?

A data lake is used to store raw data, which can include structured, semi-structured, and unstructured formats. This data can later be processed and analyzed to uncover valuable insights.

Unlike a data lake, a data warehouse is a specialized repository designed specifically for structured data. This data has been thoroughly cleaned, organized, and processed, making it readily available for analysis using analytics and business intelligence (BI) tools. The path from data warehouse to reporting is considerably shorter than the journey from data lake to reporting.

What You Get

Data warehouse design

  • Engineered data warehouse requirements.
  • Business case, recommendations on optimizing data warehouse implementation and operation costs.
  • Data warehouse solution architecture and selected DWH platform.
  • Data governance policy and design. Data governance includes:

    • Data quality
    • Data availability
    • Data security
  • Data model and ETL/ELT design.

Data warehouse development and QA

  • Customized DWH platform.
  • Integrated data sources.
  • ETL/ELT pipelines.
  • DWH performance testing and DWH launch.
  • After-launch DWH support.

Data warehouse migration / optimization / evolution

  • DWH migration / optimization / evolution strategy and plan.
  • DWH solution redevelopment on a new platform.
  • Data and metadata transfer to a new data warehouse.
  • Data completeness and accuracy assessment.
  • Data administration services: data quality and security rules and policies setup, new data sources integration, ETL/ELT processes adjustment.
  • DWH performance control: monitoring query performance, data transformations correctness, data availability.
  • DWH issues resolution.
Why Everyone Cares About Snowflake
Why Everyone Cares About Snowflake

Why ScienceSoft

  • Data analytics expertise since 1989.
  • 18 years of experience in rendering data warehouse services.
  • Designing and implementing business intelligence solutions since 2005.
  • 10 years of big data consulting and implementation practice.
  • Quality-first approach based on a mature ISO 9001-certified quality management system.
  • ISO 27001-certified security management based on comprehensive policies and processes, advanced security technology, and skilled professionals.
  • Expertise in 30 industries, including: manufacturing, healthcare, retail and wholesale, professional services, financial services, transportation and logistics, telecommunications, energy.
  • 130+ testimonials from happy customers across multiple countries.

What are the benefits of using a data warehouse?

Benefits of a data warehouse include the following:

  • Informed decision making
  • Consolidated data from many sources
  • Historical data analysis
  • Data quality, consistency, and accuracy
  • Separation of analytics processing from transactional databases, which improves performance of both systems
What is a Data Lakehouse? A Simple Explanation for Anyone
What is a Data Lakehouse? A Simple Explanation for Anyone

Frequently asked questions

  • A data lake is a centralized repository that ingests, stores, and allows for processing of large volumes of data in its original form. It can accommodate all types of data, which is then used to power big data analytics, machine learning, and other forms of intelligent action.

  • Organizations across a range of industries, including retail, finance, and entertainment, use data lake platforms to store data, gather insights, and improve the overall quality of their services. Investment firms, for example, use data lakes to collect and process up-to-market data, allowing them to manage portfolio risks more efficiently.

  • Data lakes store all types of raw data, which data scientists may then use for a variety of projects. Data warehouses store cleaned and processed data, which can then be used to source analytic or operational reporting, as well as specific BI use cases.

  • A data lakehouse combines elements of a data lake and a data warehouse to form a flexible, end-to-end solution for data science and business intelligence purposes.

  • Absolutely. Major organizations across all industries rely on the massive amounts of data stored in data lakes to power intelligent action, gain insights, and grow.

  • Large volumes of data, including raw and unstructured data, can be difficult to manage, leading to bottlenecks, data corruption, quality control issues, and performance issues. That’s why it’s important to maintain good governance and stewardship practices to help you run your data lake platform smoothly.

  • Data lake architecture refers to the specific configuration of tools and technologies that helps keep data from the data lake integrated, accessible, organized, and secure.

Key differences: data warehousesdata marts

A data warehouse is a relational database that stores data from transactional systems and business function applications. All data in the warehouse is structured or pre-modeled into tables. The data structure and schema are designed to optimize for fast SQL queries. A data mart is a different marketing term for the same technology. It is also a relational database, but practical usage differs greatly from that of a data warehouse. Key points of difference are given below.

Data sources

Data warehouses have multiple sources, both internal and external. You can extract data from anywhere, transform it into a structured format, and load it in your warehouse. Data marts have fewer data sources and tend to be smaller in size.

Focus

Data warehouses typically store data from multiple business units. They centrally integrate data from across the organization for comprehensive analytics. Data marts have a single-subject focus and are more decentralized in nature. They often filter and summarize information from another existing data warehouse.

Utilization

Multiple users and projects require the data stored in data warehouses. Hence, warehouses often have a longer lifespan and are more complex in nature. Data marts, on the other hand, may be project-focused with limited use. Teams prefer creating data marts from the enterprise data warehouse and terminating them once the use case is finished.

Design approach

Data scientists use a top-down approach when designing a data warehouse. They plan the overall architecture first and solve challenges as they arise. However, with a data mart, the data engineer already knows details like values, data types, and external data sources. They can plan the implementation from the start and take a bottom-up approach to data mart design.

Characteristics Data Warehouse Data Mart
Scope

Centralized, multiple subject areas integrated together

Decentralized, specific subject area

Users

Organization-wide

A single community or department

Data source

Many sources

A single or a few sources, or a portion of data already collected in a data warehouse

Size

Large, can be 100’s of gigabytes to petabytes

Small, generally up to 10’s of gigabytes

Design

Top-down

Bottom-up

Data detail

Complete, detailed data

May hold summarized data

Learn more about Data Warehouses

Learn more about Data Marts
Muốn làm Data Engineer: Những thứ cơ bản (và miễn phí) bạn có thể học
Muốn làm Data Engineer: Những thứ cơ bản (và miễn phí) bạn có thể học

How can AWS support your data warehouse efforts?

AWS allows you to take advantage of all of the core benefits associated with on-demand computing: accessing seemingly limitless storage and compute capacity, scaling your system in parallel with your growing amount of data collected, stored, and queried, and paying only for the resources you provision. AWS offers a broad set of managed services that integrate seamlessly with each other so that you can quickly deploy an end-to-end analytics and data warehousing solution.

The following illustration shows the key steps of an end-to-end analytics process, also called a stack. AWS offers a variety of managed services at each step.

Amazon Redshift is our fast, fully-managed, and cost-effective data warehouse service. It gives you petabyte-scale data warehousing and exabyte-scale data lake analytics together in one service, for which you only pay for what you use.

Get started with data warehouse on AWS by creating an account today.

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Data Governance Explained in 5 Minutes
Data Governance Explained in 5 Minutes

Transform Your Data Strategy with Our Data Lake and Data Warehousing Solutions

Drowning in data but starved for insights? Evolution Analytics offers robust Data Lake and Data Warehouse solutions, designed to streamline your data storage and make information retrieval effortless. Elevate your decision-making with seamless, secure, and scalable data management.

Centralized Data Storage

Bring all your disparate data sources into one unified data lake or warehouse, making it easier to manage and analyze.

High-Speed Access

Achieve lightning-fast data retrieval and analysis, eliminating wait times that can slow down decision-making processes.

Customized Solutions

Our data lake and data warehouse consultants tailor solutions to meet your specific business needs, ensuring compatibility and ease of use.

Scalability

Our solutions grow with you. Easily add new data sources and types without compromising on performance or speed.

Enhanced Security

Protect your data with state-of-the-art security protocols, ensuring that your valuable information remains confidential and secure.

Expert Guidance

From initial setup to ongoing management, our team of experts is there to guide you every step of the way.

Step into the future of data management with Evolution Analytics’ Data Lake and Warehousing consulting services and turn your data into your most valuable asset.

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All of the company’s siloed data was managed on-premises that caused issues with data governance, storage, accessibility, scalability, and more. The client engaged the N-iX team to develop a unified data warehouse on GCP, data warehouse migration, and automation of various internal processes.

The Solution

Our experts have helped the client consolidate 74 operational data sources and migrate 4 data warehouses and 1 data lake to Google Cloud. We built a unified data warehouse in GCP, allowing the client to elevate data management across the business. It allows our client to:

  • Consolidate 74 operational data sources, 4 data warehouses, 1 data lake from on-premises to Google Cloud;
  • Decommission 20 servers, leading to more than 1 million dollars in savings on MS SQL Server licenses alone.

Also, our test engineers are responsible for data quality in the data warehouse.

All of the company’s siloed data was managed on-premises that caused issues with data governance, storage, accessibility, scalability, and more. The client engaged the N-iX team to develop a unified data warehouse on GCP, data warehouse migration, and automation of various internal processes.

The Solution

Our experts have helped the client consolidate 74 operational data sources and migrate 4 data warehouses and 1 data lake to Google Cloud. We built a unified data warehouse in GCP, allowing the client to elevate data management across the business. It allows our client to:

  • Consolidate 74 operational data sources, 4 data warehouses, 1 data lake from on-premises to Google Cloud;
  • Decommission 20 servers, leading to more than 1 million dollars in savings on MS SQL Server licenses alone.

Also, our test engineers are responsible for data quality in the data warehouse.

Data Warehousing Consulting Services

Since 2005, ScienceSoft helps companies across 30+ industries consolidate disparate data into highly automated, scalable data warehouse solutions that enable timely, accurate analytics and streamline enterprise-wide decision-making.

Data warehouse consulting is expert guidance on planning, implementing, supporting, and upgrading a data warehouse in accordance with an organization’s particular needs. ScienceSoft has deep expertise in traditional and cloud DWH technologies. Our proficiency is proved by official partnerships with Microsoft, AWS, and Oracle.

What is Lakehouse Architecture?  Databricks Lakehouse architecture. #databricks #lakehouse #pyspark
What is Lakehouse Architecture? Databricks Lakehouse architecture. #databricks #lakehouse #pyspark

When to use data lakesdata warehousesdata marts?

Most large organizations use a combination of data lakes, warehouses, and marts in their storage infrastructure. Typically, all data is ingested into a data lake then loaded into different warehouses and marts for assorted use cases. The technology decision depends on various factors as explained below.

Flexibility

In general, data lakes offer more flexibility at a lower cost. Different teams can access the same data using their choice of analytic tools and frameworks. You can save time as there is no need to define data structures, schema, and transformations.

Data types

A data warehouse is better if you want to store relational data like customer and business process data. If you have a large volume of relational data, your team may consider creating some data marts for specific business needs. For example, the accounts department may create a data mart to maintain balance sheets and prepare customer account statements, while the marketing department may create another data mart for optimizing advertising campaigns.

Cost and volume

A data warehouse can efficiently handle hundreds of petabytes (PB) of data. Data lakes offer a comparatively lower cost for more volume, especially for large numbers of images and videos. However, not every organization may require that level of scale.

Key differences: data warehousesdata lakes

A data warehouse and a data lake are two related but fundamentally different technologies. While data warehouses store structured data, a lake is a centralized repository that allows you to store any data at any scale. A data lake offers more storage options, has more complexity, and has different use cases compared to a data warehouse. Key points of difference are given below.

Data sources

Both data lakes and warehouses can have unlimited data sources. However, data warehousing requires you to design your schema before you can save the data. You can only load structured data into the system. Conversely, data lakes have no such requirements. They can store unstructured and semi-structured data, such as web server logs, clickstreams, social media, and sensor data.

Preprocessing

A data warehouse typically requires preprocessing before storage. Extract, Transform, Load (ETL) tools are used to clean, filter, and structure data sets beforehand. In contrast, data lakes hold any data. You have the flexibility to choose if you want to perform preprocessing or not. Organizations typically use Extract, Load, Transform (ELT) tools. They load the data in the lake first and transform it only when required.

Data quality

A data warehouse tends to be more reliable as you can perform processing beforehand. Several functions like de-duplication, sorting, summarizing, and verification can be done in advance to assure data accuracy. Duplicates or erroneous and unverified data may end up in a data lake if no checks are being done ahead of time.

Performance

A data warehouse is designed for the fastest query performance. Business users prefer data warehouses so they can generate reports more efficiently. In contrast, data lake architecture prioritizes storage volume and cost over performance. You get a much higher storage volume at a lower cost, and you can still access data at reasonable speeds.

Characteristics Data Warehouse Data Lake
Data

Relational data from transactional systems, operational databases, and line of business applications

All data, including structured, semi-structured, and unstructured

Schema

Often designed prior to the data warehouse implementation but also can be written at the time of analysis

(schema-on-write or schema-on-read)

Written at the time of analysis (schema-on-read)

Price/Performance

Fastest query results using local storage

Query results getting faster using low-cost storage and decoupling of compute and storage

Data quality

Highly curated data that serves as the central version of the truth

Any data that may or may not be curated (i.e. raw data)

Users

Business analysts, data scientists, and data developers

Business analysts (using curated data), data scientists, data developers, data engineers, and data architects

Analytics

Batch reporting, BI, and visualizations

Machine learning, exploratory analytics, data discovery, streaming, operational analytics, big data, and profiling

Learn more about Data Warehouses Learn more about Data Lakes
Data Engineer's Lunch 106: Designing an analytics pipeline with BigQuery and dbt: a walkthrough
Data Engineer’s Lunch 106: Designing an analytics pipeline with BigQuery and dbt: a walkthrough

When to Use Data LakesData Warehouses

Choosing between data lakes and data warehouses is an important decision in the world of data management, each has its strengths and best-use characteristics. Consider the following common scenarios when trying to decide whether a data lake or data warehouse is more appropriate for your needs.

Data Lake Use Cases

  • Centralized Repository for Business Data: Data lakes can handle vast amounts of data cost-effectively, thanks to their scalability and versatility in accommodating various data types. This allows businesses to store significantly more data in data lakes compared to data warehouses, all without the constant concern of cost optimization.
  • IoT Data Storage: IoT devices produce enormous amounts of data, according the the International Data Corportation, the Global DataSphere is expected to double in size from by 2026 with about 45% of data attributed to IoT devices alone. Data lakes are well-suited for storing this data for analysis. This storage capability assists organizations in optimizing operations, enhancing product performance, and elevating customer experiences.
  • Data Exploration and Data Discovery: Data scientists and analysts can explore raw data in data lakes to discover new patterns, trends, and insights. Since data lakes can store diverse data types, they provide a playground for exploratory data analysis.
  • Big Data Processing: Data lakes can store vast amounts of raw data from multiple sources, enabling organizations to perform complex big data analysis, predictive modeling, and machine learning algorithms on the data.
  • Real-Time Analytics: Data lakes can handle real-time data streams, allowing businesses to analyze and gain insights from data as it is generated. This is particularly useful in industries such as finance and online retail, where real-time decisions are crucial.
  • Data Warehousing Offloading: Organizations can use data lakes to store raw data before it’s transformed and loaded into a data warehouse. This helps offload the ETL (Extract, Transform, Load) processes, making it more efficient and cost-effective.

Data Warehouse Use Cases

  • BI and Reporting: Data warehouses provide a centralized, structured database for historical and current data. Businesses can use this data to generate reports, visualize trends, and gain insights into their operations. This is crucial for making informed business decisions.
  • Historical Trend Analysis: The data warehouse can store historical data from multiple sources, representing a single source of truth. Data warehouses enable businesses to analyze trends over time. This analysis aids in understanding long-term patterns in sales, customer behavior, website traffic, and more, assisting businesses in making data-driven decisions.
  • Natural Language Processing (NLP): Many organizations seek to enhance customer service via NLP, as it facilitates rapid analysis and can help boost growth in support, sales, and marketing. Data warehouses can effectively store extensive structured and unstructured data, enabling NLP model analysis. This analysis supports real-time responses, whether by internal staff or bots, like live chat assistance or personalized customer interaction based on historical data.
  • Compliance and Regulatory Reporting: Industries such as finance and healthcare must adhere to strict regulatory requirements. Data warehouses aid in collecting, storing, and analyzing data necessary for compliance and regulatory reporting.
  • Financial Analysis and Planning: Finance departments utilize data warehouses for financial reporting, budgeting, and forecasting. These tools enable detailed analysis of financial data, helping organizations plan and allocate resources effectively.
  • Healthcare Analytics: Healthcare organizations use data warehouses to store patient records, medical histories, and treatment outcomes. Data warehouses enable healthcare professionals to analyze this information, improving patient care, treatment effectiveness, and resource allocation.

Lakehouses

Lakehouses combine features of data lakes and data warehouses to overcome the limitations of data lakes. These limitations include slow query performance, data consistency and security, and data integrity. Lakehouses can overcome these issues with the following:

  • Schema Enforcement – A lakehouse architecture defines and enforces a schema to improve query performance and foster easier data analysis by users.
  • Data Consistency – Lakehouses support ACID (Atomicity, Consistency, Isolation, Durability) transactions as well as versioning. Companies can implement data quality measures to ensure accurate and reliable data.
  • Scalability – Lakehouses are built upon cloud storage accounts which can be easily scaled up to handle increased data volumes. Data integration to shape the defined schemas is based on engines such as Apache Spark. Those technologies are easily scaled by adding additional compute nodes to handle increased workloads.

Similar to data lakes, tools such as Power BI can be used to ingest and analyze these lakehouse models. The primary advantage is that the refined lakehouse model can be imported and used without the need to define schema and shape the data as part of the data import. This makes the model easier to understand and use by less-technical users.

Data Lakes in the Cloud
Data Lakes in the Cloud

How does a data mart compare to a data warehouse?

A data mart is a data warehouse that serves the needs of a specific team or business unit, like finance, marketing, or sales. It is smaller, more focused, and may contain summaries of data that best serve its community of users. A data mart might be a portion of a data warehouse, too.

For an in-depth comparison between data mart and data warehouses visit our dedicated comparison page for data mart vs data warehouse.

How Consulting Helps Reduce Data Warehouse Costs

  • 30%

    project time and budget cost reduction due to thorough project management.

  • Up to 60%

    less IT staff time to deploy, administer and support a DWH solution due to choosing an optimal DWH platform.

  • Minimized

    infrastructure costs. No risk of infrastructure overprovisioning due to choosing proper DWH architecture, software, cloud vendor, cloud service configurations, etc.

More from ScienceSoft

Services

Data Warehousing

Data Science

Big Data Technologies

Solutions

Business Intelligence

Big Data

Data Management

Microsoft Business Intelligence

Big Data Databases

Unleash the potential of your data with a warehouse in the cloud

Traditional data warehouses can be costly, complicated and inflexible. Modernize your growing repositories of data by building in a cloud structure designed to clean and prep your data for analysis – so you can discover deeper insights that drive your business forward.

A combination of a cloud data warehouse and data lake in the cloud supports the highly advanced data analytics that can position your organization for the future. With our experience and expertise, we can help you imagine the possibilities, identify use cases and get you to a minimal viable product (MVP).

Data Lake Architecture
Data Lake Architecture

Benefits and Disadvantages of a Data Lakea Data Warehouse

There are two sides to every coin. The advantages of data lakes and data warehouses come with equal, opposing disadvantages. Knowing which solution is right for your data, along with the benefits and drawbacks, can help you decide how your data needs to be housed.

Data Lake Benefits

Data lakes offer flexibility because they can store raw data in any format. Like resources within a cloud-first strategy, they can be scaled up or down on demand, and they can be a cost-effective solution for storing lots of data.

Data Lake Disadvantages

However, the costs you save in storing data can be canceled out by the costs involved in querying the and finding what you need. There’s no predefined schema, which increases the complexity of managing a data lake as it makes the data more difficult to query. Other challenges include:

  • They can be less secure and have lower performance
  • It can be a struggle to ensure the quality of the data being added to the data lake
  • Data that’s never analyzed or mined may take up unnecessary space

Data Warehouse Benefits

Querying with data warehouses is much more efficient, making it easier for businesses to take the available data and make quick decisions. If users understand the predetermined schema, data warehouses are easier to use. Oftentimes, there are more stringent security measures in place as well.

Data Warehouse Disadvantages

The time saved when using a data warehouse can bring cloud waste or unnecessary costs down, but it’s important to remember that storing data in a structured format can cost more than a data lake. Data warehouses are also less scalable because they use a predefined schema that isn’t as flexible. Other challenges include:

  • Since data warehouses are information-driven, there needs to be a significant amount of time dedicated to standardizing business-related terms and common formats, as well as restructuring schema to alsign with business needs while ensuring data accuracy
  • Proper planning and setting up data orchestration is critical – an outline needs to be created of how to copy data from source systems to the warehouse, as well as when to migrate historical data from operational data stores to the warehouse
  • Data needs to be cleaned as it’s imported into the warehouse to maintain data quality

Highlights of Our Data Warehouse Consulting Services

Multidisciplinary expertise

Our data warehouse consulting team consists of:

  • Project managers.
  • BI consultants.
  • DWH architects.
  • Data quality experts.

Effective communication

  • One-to-one sessions with project stakeholders.
  • Meetings with several or all stakeholders to reconcile conflicting expectations.
  • Presentations of important project decisions, deliverables, risks, or project milestone results.
  • Cross-departmental workgroups to solve complex problems (e.g., related to data quality, master data management).
KNOW the difference between Data Base // Data Warehouse // Data Lake (Easy Explanation👌)
KNOW the difference between Data Base // Data Warehouse // Data Lake (Easy Explanation👌)

Why are data lakes important for businesses?

Today’s highly connected, insights-driven world would not be possible without the advent of data lake solutions. That’s because organizations rely on comprehensive data lakes platforms, such as Azure Data Lake, to keep raw data consolidated, integrated, secure, and accessible. Scalable storage tools like Azure Data Lake Storage can hold and protect data in one central place, eliminating silos at an optimal cost. This lays the foundation for users to perform a wide variety of workload categories, such as big data processing, SQL queries, text mining, streaming analytics, and machine learning. The data can then be used to feed upstream data visualization and ad-hoc reporting needs. A modern, end-to-end data platform like Azure Synapse Analytics addresses the complete needs of a big data architecture centered around the data lake.

Our Customers Say

Heather Owen Nigl

Chief Financial Officer

Alta Resources

We first contacted ScienceSoft to get expert advice on the creation of the centralized analytical solution. After we got a clear project roadmap, we commissioned ScienceSoft to develop a part of the solution, covering invoicing. The system automates data integration from different sources and provides visibility into the invoicing process. We have already engaged ScienceSoft in supporting the solution and would definitely consider ScienceSoft as an IT vendor in the future.

Donat Gaudreau

Electrochemical Cell Design and Test Engineer

Unilia Fuel Cells

We commissioned ScienceSoft to build a flexible database with user interfaces for managing our test data stored as time-based CVS files. ScienceSoft delivered a fully functioning solution regardless of the new requirements that appeared during the project. We are planning to extend the logic of our reports and dashboards and data processing options in our solution, and we’ll definitely be considering ScienceSoft as our partner in this initiative.

Maria Zannes

President & CEO

bioAffinity Technologies

bioAffinity Technologies hired ScienceSoft to help in the development of its automated data analysis software for detection of lung cancer using flow cytometry. Our project required a large amount of industry-specific methodology and algorithms to be implemented into our new software connected to EHR/LIS systems, which ScienceSoft’s team handled well due to a profound understanding of laboratory software specifics and integrations.

Having expertise in various data storage solutions, like on-premises and cloud data lakes, data warehouses, and data lakehouses, we don’t promote any as a universal problem-solver. For instance, a data warehouse is excellent for BI and analytics needs, but it will definitely fail to provide a cost-efficient storage for multi-structured data. That is why, ScienceSoft’s best practice in data warehouse projects is to make sure that data is handled in an optimal way along all the stages, including data ingestion, raw storage, transformation, and aggregation. And we choose the best-fitting techs depending on multiple factors (e.g., data volume, complexity, and diversity) and customers’ unique processes.

Exploring the Differences Between HANA Cloud Database, Data Lake, and Data Warehouse
Exploring the Differences Between HANA Cloud Database, Data Lake, and Data Warehouse

How do data warehouses, databases, and data lakes work together?

Typically, businesses use a combination of a database, a data lake, and a data warehouse to store and analyze data. Amazon Redshift’s lake house architecture makes such an integration easy.

As the volume and variety of data increases, it’s advantageous to follow one or more common patterns for working with data across your database, data lake, and data warehouse:

Unlike a data warehouse, a data lake is a centralized repository for all data, including structured, semi-structured, and unstructured. A data warehouse requires that the data be organized in a tabular format, which is where the schema comes into play. The tabular format is needed so that SQL can be used to query the data. But not all applications require data to be in tabular format. Some applications, like big data analytics, full text search, and machine learning, can access data even if it is ‘semi-structured’ or completely unstructured.

For an in-depth comparison between data warehouses and data lakes, visit our dedicated comparison page for datawahouse vs data lake.

Data Lakes: An In-Depth Explanation - Cmc Global
Data Lakes: An In-Depth Explanation – Cmc Global
Data Lakes Vs. Data Warehouses
Data Lakes Vs. Data Warehouses
What Is A Lakehouse? | Databricks Blog
What Is A Lakehouse? | Databricks Blog
Data Lake Vs Data Warehouse: What Should Your Organization Choose – Nix  United
Data Lake Vs Data Warehouse: What Should Your Organization Choose – Nix United
Data Storage Explained: Data Lake Vs Warehouse Vs Database – Bmc Software |  Blogs
Data Storage Explained: Data Lake Vs Warehouse Vs Database – Bmc Software | Blogs
Bdcc | Free Full-Text | An Overview Of Data Warehouse And Data Lake In  Modern Enterprise Data Management
Bdcc | Free Full-Text | An Overview Of Data Warehouse And Data Lake In Modern Enterprise Data Management
Data Lake Vs Warehouse: Designing Infrastructure For Analytics
Data Lake Vs Warehouse: Designing Infrastructure For Analytics

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