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

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.

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.

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 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 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

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

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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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.

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

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.

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.

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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).

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).

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.

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.

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