Published: August 1, 2022

Effective business intelligence (BI) is critical for enterprises to generate revenue and maximize their ROI. With data increasing in complexity and volume every day, businesses need something more out of their BI software than a traditional database to store historical and transactional information for analytics. Many companies want live insights as well, and analyzing big data in real time requires a solution beyond a regular database. Data warehouse is a big data storage system that enable data management for enterprise reporting and data analytics.

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Importance

Why are data warehouses important? Conventional databases rely on online transaction processing (OLTP) for managed reporting, but they are not big on analytics. OLTP applications do not work well for business intelligence and data analysis tasks since they have high throughput with a large number of read and write requests. On the other hand, data warehouses leverage online analytical processing (OLAP) to analyze historical and live data on the same unified platform. You can access SQL as well as NoSQL data through flexible data querying. Data access is quicker through data warehouses thanks to in-memory processing, columnar storage and data compression.

Oracle Warehouse Builder, Microsoft SQL Server Integration Services, Pentaho Data Integration and Jasper ETL are leading ETL data warehouse solutions. In addition to data modeling, these tools provide advanced data load options, governance and configuration management all on a single platform. A high-performing and productive data warehouse system provides reusable components and end-to-end data lineage and impact analysis.

Data Warehousing vs. ETL Tools

While data warehouses are repositories of business information, ETL (extract, transform and load) is a process that involves extracting data from the business tech stack and other external sources and transforming it into a structured format to store in the data warehouse system. Though traditionally, ETL tools have worked with a staging area to store the data temporarily and transform it, newer approaches are changing all that.

Analytics warehouses like Amazon Redshift and Google BigQuery can host data transformations in place, eliminating the need for a staging area. Many cloud-based applications now store business-critical data internally, and ETL tools can transform it where it resides through API tools and webhooks. Besides, extract, load and transform (ELT) processes enable businesses to analyze data in its raw form through schema-on-read, and ETL processes, as a result, are more lightweight, flexible and transparent.

Data Warehouses vs. Databases vs. Data Marts vs. Data Lakes

How are databases, data lakes and data marts related to data warehouses? Databases, as discussed above, are relational data storage solutions but they are not built for analytics. Data lakes are where you dump all your raw data after extracting it, while data warehouses store data that is structured and analysis-ready. Data marts are subsets of data warehouse solutions, and they store data pertaining to a specific department, making it available to end users on demand. In some cases, the departments own their data mart and control the hardware, software and data.

A company might often decide to go for a combination of any of these storage solutions, depending on the data types that they manage and the time-to-insight window available to them.

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Key Benefits

Data warehousing solutions are the mainstay of an enterprise’s data management process. Here are some of their key benefits.

Get All Your Data In One Place

Organizing your company’s data in one place can speed up reporting and analysis, in addition to ensuring accurate, unique data as a single source of truth. While traditional databases that your company uses might be siloed, a data warehouse system can seamlessly integrate with a wide range of data repositories that include databases, data lakes and other data warehouses. It can store data pulled from the web, cloud-based applications and data stores, IoT and streaming devices, and mobile phones, giving you a more complete picture.

Make Better Business Decisions

With all your data in one place, you can access it quickly and view business metrics on demand. You can glean focused business insights and make decisions in a fraction of the time compared to conventional databases. Like other business intelligence software, modern data warehousing solutions provide self-service capabilities, empowering people of all technical skills across your organization to share insights and participate in decision support.

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Scalability

Cloud data warehouses are equipped with compute control and can scale compute services up and down as needed according to varying workload demands. You can even set an auto-suspend timeout to shut the data warehouse system down when left idle for a specified period, saving on usage. They provide many other resource optimization capabilities, such as tackling the “noisy neighbors” issue. These are tasks that tend to monopolize resources; workload isolation helps reserve resources and keeps them siloed for specific workflows. Most cloud-based software vendors offer pay-as-you-go models with easy scalability, which work well for enterprises of all sizes.

Integrations

Modern data warehousing solutions integrate with existing BI tools to provide a comprehensive data analytics solution to your business. With software such as Alteryx and Domo, your developers can build custom apps, automate data pipelines and share actionable insights with others across your organization. Azure’s next-gen data warehouse system, Synapse Analytics, empowers business users to query against their big data and perform analytics at scale, all on a single platform.

Use Cases

This report by the Enterprise Strategy Group states that improvements in BI and Analytics drove the maximum spend for enterprises in 2019. A data warehouse satisfies two critical modern-day enterprise needs – big data storage and dynamic data analysis. So, who uses data warehouses?

Use cases include organizations that need data from siloed databases for cumulative market research and advanced analytics. With data warehouses, data scientists can create user-friendly references for specific data sets, simplify relationships by restructuring the schema and make tables easy to understand by joining them. By doing so, users can create custom and ad-hoc reports and analyze the data in BI systems without needing the help of a database administrator.

Many companies leverage data warehouses for better performance and data quality; for instance, they might need to keep historical data separate from the transactional source systems. Conventional databases can slow down when queried since they aren’t optimized for read access. Data warehouses enable access to historical information from multiple locations by providing a centralized platform with standard data formats and models. They help abstract actual business systems and databases from direct data manipulation by virtualizing data.

Companies that are already using data warehouses but also need streaming data and IoT analysis can benefit from using end-to-end solutions that incorporate data integration, analysis and visualization in one suite. Take, for instance, the Amazon suite of products – you can pull data with Amazon Kinesis Data Firehose, analyze it in Amazon Redshift and visualize the results in Amazon Quicksight through interactive dashboards.

If your company’s data needs match any of the above, you will likely need a data warehouse system. If you already have one and are looking for a better option, you should make a list of your company’s current requirements and identify the reasons why your current solution falls short. Doing this will give you a better idea of what you need in a data warehouse.

Architecture

How does a data warehouse system work? Data in business systems is replete with valuable information, but delivering it to you, the user, on time is critical.

The three-tier architecture is the most common architecture model for data warehouses.

Three-Tier Model

Let’s look at what each tier entails.

• The Presentation Tier: This tier displays information to and collects data from the user and can run on a web browser, as a desktop application or a graphical user interface (GUI). On the front end, business analysts, data scientists and decision-makers access this data by using query tools that include business intelligence software and analytics solutions.
• The Application Tier: This is the logic tier and processes the information collected from the presentation tier using a specific set of business rules. Developed using Python, Ruby, Java, Perl or PHP, it communicates with the data tier using API calls. Often, this is an OLAP server.
• The Data Tier: The information processed by the application tier is stored and managed in the data tier, usually a relational database. An enterprise data warehouse stores on-premise information and data captured from ERPs, CRMs and other external applications, including cloud-based ones.

Each tier runs on its own architecture and can be managed and scaled as needed.

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Shifting Trends

The global data warehousing market is growing at a CAGR of 10.7% every year, driven by the need for dedicated storage systems and big data analytics in real time with low latency. Below are some of the current trends that have disrupted the data warehouse market.

Augmented Analytics

Machine learning (ML) and artificial intelligence (AI) have been game changers for data warehouses. Modern data warehouses provide access to ML-enabled analytics tools with Python and R at the back end for transforming large volumes of data where it resides. Take the example of Snowflake, a built-for-the-cloud data warehouse – it works with Azure Databricks, a unified analytics platform, to provide faster ML-based insights. Agile and innovative data insights are now possible, differentiating your business from the competition. Machine learning enables automated data discovery through algorithm selection and data modeling to analyze current and future trends.

Cloud-Based Solutions

With more businesses opting for distributed workplaces, users are increasingly shifting to cloud-based software and technologies. Enterprises want integrated data solutions to connect to live data where it resides, and transform, analyze and visualize it on a single platform. Additionally, businesses want more transparency regarding the data sources, the transformation process, and the logic applied to data through self-service analytics. The cloud provides a simplified data management solution with data integration, warehousing and analytics on one platform, and this trend is likely to continue.

Needs Analysis

Before considering a data warehouse system for your organization, create a list of your requirements by discussing your business needs with your team. Here are some of the questions that you can ask of all stakeholders to fine-tune your requirements list and software search.

• Do you work with large data volumes and complex queries?
  Massively parallel processing (MPP) solutions are more suited to handle analytical, batch-processing workloads; MPP refers to multiple processors performing computations in parallel.
  For transactional workloads and smaller read/write operations, symmetric multiprocessing (SMP) analytic engines might be the thing for you. SMP involves multiple processors connected to a single, shared main memory, the same I/O devices and controlled by a single OS, ensuring uniform access to all processors.
• Is your data set structured, or does it include unstructured data as well? Big data analytics engines such as Hadoop, Spark and Azure Databricks are built from the ground up to process all big data types, including unstructured data.
• Do you need to keep your historical data separate from your transactional data? Standalone data warehouses are optimized for large read loads and have separate storage for historical data.
• Do you have real-time reporting requirements? Many modern data warehouses support real-time data analysis with automated, periodic data refreshes to help keep your business information up-to-date.
• Will the data warehouse system integrate with your existing analytics tools? Modern data warehouse system vendors provide SDKs and APIs to integrate the tool with many leading BI and analytics solutions.

You can add to this list by consulting your data management team and other stakeholders in your company.

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Wrapping Up

According to this Forbes article, data warehouse optimization is the top use case for big data and is considered critical by almost 70% of organizations. Cloud data warehousing is the next big thing in data management, with elasticity, scalability, managed systems, faster deployment and processing, and cost savings. And computational storage and storage class memory are the data processing trends to watch out for in the coming years. If you are considering a data warehouse system, you can get references from industry peers or get our free requirements template to get you started.

For a feature-by-feature comparison of data warehousing products, you can refer to our Decision Platform. Once you have a product shortlist ready, you can contact the respective vendors for trials, demos and the pricing information to help you decide, or you can reach out to us to help guide you through the selection process.

What data storage solutions do you use? Do you use a data warehouse? Let us know in the comments!