Best data warehouse solutions: Professional overview by DICEUS
In the big data era, it is impossible to imagine a well-functioning business without a serious well-managed digital infrastructure. Modern data warehouse solutions (DWS) provide great opportunities for storing and managing terabytes of information, ensuring the development of entire industries and even small countries.
Today, DWS is used by enterprises of all sizes to improve the quality of business intelligence, enhance their decision-making capabilities, and gain the best competitive advantage. According to forecasts, the volume of the global DWS market will grow at an average annual rate of 10.7% and will reach $51.18 billion by 2028.
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Main factors contributing to this growth include:
- The need for a dedicated storage system
- The demand for column-oriented data warehouse solutions to perform advanced analytics
- The need for minimal latency, real-time viewing, and analytics of operational data
Market growth will also be driven by increased enterprise awareness of the growing data streams from various sources and the introduction of modern business intelligence tools. New industry trends involving virtual storage and AI will surely provide tremendous benefits.
The question is, which data warehouse solutions are the best? Due to the COVID-19 epidemic, many providers pursue multiple business and marketing strategies, updating their product portfolios with customized offers. Google LLC did just that, providing speed-boosting capabilities for its BigQuery cloud storage, allowing analysts to complete tasks five times faster.
At DICEUS, we offer our review of the best DWS.
More insights:
Best DWH (data warehouse) solutions on the market and why you may need them
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How big data and business intelligence benefit e-commerce
What is a data warehouse?
The data warehouse (DWH) is an electronic database designed to collect data from various sources, analyze and manage them.
DWH is a channel between operational data warehouses and supports composite data analytics. Fragments of data from the warehouse, which are stored in the “data mart” for quick access, can be used across enterprise departments. To effectively support decision-making, data is retrieved from various sources and loaded into the warehouse.
DWH can be organized into tables, stripped of redundancy, and transformed for consistency using ETL extraction, transformation, and loading. It stores structured data, which is well-organized and searchable. It can be easily accessed by anyone in the organization. It is predictable, reliable, and applicable in most business environments because they provide the following:
- Fast indexing, cataloging and searching
- Easy launch and support
- Accessibility for business users
It is the core of a business intelligence system built for data analysis and reporting. Storage costs are high, so they are not best for storing large volumes, semi- and unstructured data.
Types of data warehouse solutions
There are three main types of data stores:
Enterprise data warehouse (EDW)
It is a centralized corporate repository. It provides a unified approach to organizing and presenting data suitable for enterprise-wide decision-making support. EDW also allows to categorize of data and provides access according to different sections.
Operational datastore
An operational Data Warehouse (ODS) is a database that integrates data from disparate sources to be further used by reporting applications. The data is updated in real-time, so that it can be conveniently used for routine actions.
Data mart
It is a part of the data warehouse dedicated to a specific direction, such as sales or finance. In an independent storefront, you can collect data directly from sources.
According to the way of implementation, DWH can be both physical and virtual.
Data warehouse features: how it all works
The data warehouse acts as a central repository where information comes from one or more sources, such as a transactional system and relational databases. By combining information, an organization can comprehensively analyze customers. The warehouse enables data mining to identify patterns leading to increased sales.
The data can be structured, semi-structured, and unstructured.
It is processed, transformed, and loaded so that users can access the information in the warehouse using business intelligence tools, SQL clients, and spreadsheets.
Specialized solutions often include a number of useful features for managing and consolidating data. You can use them to extract and use data from different environments, transform data and remove duplicates, and ensure consistency in analytics. Some contain machine learning algorithms and built-in artificial intelligence.
Main functions and capabilities of DWH
- Associated tools for entering, adding, extracting, and manipulating data
- Extraction capabilities from many types of source files
- Ability to load and normalize structured, semi-structured, or unstructured data
- Data transformation (cleanup, deduplication, consistency)
- Data consistency for different naming conventions
- Built-in and offline storage and processing optimization
- Complete overview of all corporate data
- Multiple deployment options (private, public, on-premises, hybrid cloud)
- Available as a Service (Automated Infrastructure Management)
- Integrated machine learning algorithms, AI
- Sharing data with controlled access
- Built-in encryption for high data security
When delivered over the cloud, solutions are even more flexible. As with other as-a-service environments, business leaders can add and remove features to meet changing needs.
Best data warehouse solutions
There are quite a few good DWHs. However, below we gathered the most popular ones.
Amazon Redshift
Amazon Redshift is one of the most popular solutions on the market nowadays. The service supports the analytical initiatives of most Fortune 500 companies, including such brands as McDonald’s, Yelp, Intuit, and Lyft.
One of the benefits of Redshift is its perfect integration with the data lake and AWS environment. The solution allows you to query huge amounts of structured and semi-structured data using a variety of settings. For ETL outside of S3 Data Lake, you can use AWS Glue.
It is fast, fully managed petabyte storage that efficiently analyzes data using existing analytics tools. It is optimized for datasets ranging in size from several hundred gigabytes and costs ten times less than most traditional DWHs.
Snowflake
A global cloud solution Snowflake offers a wide range of technologies. We can say that today it is one of the best cloud data warehouses. A robust architecture simplifies and improves the data pipeline while reducing unnecessary complexity. You get self-service access to all the additional features you need. Wherever your data or users are, Snowflake delivers a seamless experience across multiple public clouds. Unlike other services, there is an option of pay per second available.
Learn more about data warehouse in banking industry
Google BigQuery
Initially, the component of the Google cloud platform. The highly scalable, serverless cloud-based data warehouse is perfect for companies looking to make informed decisions with data analytics and at lower costs.
With this solution, you wouldn’t need an administrator. BigQuery is affordable. It makes it easy to query data using SQL and Open Database Connectivity. It allows you to run your analytics environments. At the same time, the combined three-year cost of ownership is up to 34% cheaper than other cloud alternatives. Another significant benefit is the integration with Google’s machine learning tools.
IBM Db2 Warehouse
The best relational database offering that provides high-quality analytics and data management solutions. ODS is committed to providing actionable information and data accessibility to the companies that need it most. There is also support for a number of operating systems.
Db2 integrates with IBM’s in-memory columnar database engine. This is a definite advantage for companies that require high productivity. Business leaders can initiate cloud deployments using AWS or the IBM Cloud. A local version of the repository is also available. Db2 is supported on Linux, Unix, and Windows.
With Db2, you can deploy data wherever you need it. You can flexibly adapt to changing needs and integrate with multiple platforms, languages, and workloads.
Microsoft Azure Synapse
An enhanced version of Microsoft Azure SQL Data Warehouse. It is a state-of-the-art analytics solution that combines enterprise data warehousing with the latest advances in big data analytics. With Microsoft technologies, you can easily request data according to any of your needs. There is access to both provisioned and serverless resources on demand.
Synapse unlocks the potential of machine learning and business intelligence as part of a complete data infrastructure. Importantly as well, Microsoft guarantees some of the most advanced security and privacy features in the industry.
It is a cloud-based EDW that uses Massively Parallel Processing (MPP) to quickly execute complex queries. Azure Synapse Analytics helps provide insight and analytics on stored data that can be used to make further decisions.
This gives you the freedom to request data on your terms using serverless or dedicated resources at any scale. Ideal for data migration, orchestration, ETL pipeline, integration with other services such as Powerbi, Cosmos DB, and Azure ML.
Oracle Autonomous Data Warehouse
This warehouse solution offers an affordable and easy-to-use system that scales with operations. The solution provides fast and flexible query performance without tedious administration. It is a fully managed cloud service that simplifies access to data warehouse resources for both newbies and existing Oracle fans.
Autonomous data storage is highly elastic, allowing you to expand and update computing power as your business grows. Moreover, all this is integrated with a range of business intelligence tools and the Internet of Things.
SAP Cloud Storage
The ideal solution for enterprises making complex business decisions. Enterprise DWH brings together unique sources of information into a single environment to improve the security and reliability of your data.
The semantic storage layer makes it easy to maintain custom analytics. Built-in IBM adapters provide instant access to data. Flexible, scalable storage meets the needs of any enterprise.
You might be interested in reading our article “Oracle FLEXCUBE core banking system: Benefits and features”
Case study: how we developed a DWH for a bank
One of our customers, a Jordanian bank that offers a wide range of financial services, including online payments and mobile banking, approached us with a request to create a DWH with a robust architecture. It is one of the fastest-growing banking structures in Jordan, looking to keep pace with the current global economy and technological advances.
They had a pressing need to validate the information from reports, clarify business reporting needs, and improve storage architecture to achieve a smooth and consistent workflow. The task was to develop and integrate DWH solutions and implement a liquidity risk management system for cooperation with the European Bank for Reconstruction and Development. The ultimate goal was to develop basic analytic and reporting dashboards.
The project was managed by managers with PMI certifications. A serious technological stack was used ─ PL/SQL, Oracle Apex, Oracle SQL Developer, Git, Jira, and Microsoft Project. In the shortest possible time, we presented a new design of the storage architecture ─ the software core of the management reporting system based on Oracle UBS FLEXCUBE in partnership with the Frankfurt School of Finance and Management International Advisory Services.
DICEUS specialists implemented a complex data warehouse for connecting and analyzing business data from 15 heterogeneous sources based on their information model with data marts for the entire bank workflow.
We have developed and implemented a process for regular monitoring and analysis of key banking operations, and reporting systems for loan and deposit portfolios. Several tests were carried out, and the project was delivered on time.
Our work has reduced the load on commercial transaction systems and paved the way for optimized integration in the future. The multi-level reporting system on Oracle BI makes it possible to track every operation and transaction. We’ve created a single and unified source of information for scalable and reliable reporting with an interface that provides all the easy-to-navigate elements.
As a result, the client got what he never had before – a data model for in-depth analysis, strategic planning, and forecasting. We have enhanced DWH to ensure efficient workflow and information reliability. Thanks to automation, the solution is ideal for consistent monitoring and analysis of key banking transactions in real time.
DWH helps solve many business problems with useful data management and consolidation features. Some of them use machine learning algorithms and built-in artificial intelligence. Users get the opportunity to not only manage their data and analytics, they also get the benefits they could only dream of in the past. If you want to take your business to the next level, use the optimal DWH solution without waiting for your competitors to do it first.
Here at DICEUS, we have the necessary expertise and an extensive portfolio of successful projects, so we can guarantee to help you achieve all your goals.
