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Guide to Data Warehouse Modernization: Methods, Drivers, and Approaches

By |2021-11-04T11:23:31+00:00May 3rd, 2021|

We often hear the phrase: the data warehouse is evolving and modernizing. But have you ever wondered what this means and how it affects your business? This blog will answer these critical questions, what is data warehouse modernization, the benefits of modernizing your data warehouse, and why it is essential for enterprises to modernize the data warehouse.

What is Data Warehouse Modernization?

Data warehouse modernization is all about revamping and extending your data warehouse infrastructure to reap the benefits of new technologies, inducing speed and agility in your data processes, meet changing business requirements, and stay relevant in this age of big data.

A data warehouse is there to help you make better decisions. But long gone are the days when a daily report at day’s end would be good enough to meet most of your business’s demands. Businesses now need valuable insights and reports in real-time, and legacy data warehouses cannot keep up with these modern data demands; neither were they designed to do so.

The growing variety, volume, velocity, and veracity of modern data make it essential for businesses to modernize their data warehouses to remain competitive in today’s fierce market.

Methods of Data Warehouse Modernization

How do you go about data warehouse modernization? Do you rip everything out and rebuild from scratch, or do you add on top of your existing data warehouse? You can do both, actually. Here are the three most effective methods of data warehouse modernization:

1. Move from on-premise to the cloud

The first method is to move your on-premise legacy systems to a cloud-based data warehouse. There are several advantages of this approach, including:

· The pay-as-you-go model of the cloud helps reduce costs significantly – you only pay for the storage and compute that you use.

· Higher elasticity since you can easily scale a data warehouse on the cloud as the volume of data increases.

· Zero maintenance and support costs.

· Integrations with other cloud-based services and applications are much easier and quicker.

Keeping in mind these benefits, we recommended this approach for businesses that want to reduce the high costs and complexity of maintaining on-premise infrastructure. In fact, statistics even show that a majority of data warehouses are now already partially or completely on the cloud.

2. Start a new project

This method involves using modern tools, platforms, and practices to launch a new data warehouse initiative. For example, if you plan to launch an analytics initiative for a business unit, it can be a good opportunity to launch this new project with modernization in mind.

In contrast to launching a new analytics project using legacy systems, the benefits of this method are:

· Support for an agile and flexible development effort since modern tools and cloud platforms allow you to experiment, test, and evolve ideas rapidly without heavy investment or long development times.

· Better support for DW automation tools and solutions such as Astera DW builder allows you to scale and expand your analytics effort with minimum effort.

If you are yet to build a data warehouse or feel that your existing data warehouse will not be able to support your next analytics initiative, then this approach is recommended to ensure better performance and speedy results.

3. Extend your existing data warehouse

Sometimes, businesses might want to keep their on-premises and legacy systems for reasons such as compliance and security. But this does not mean that they cannot explore the benefits of modernization. In such scenarios, extending your existing data warehouse is the recommended method to modernize your data ecosystem.

In this method, you integrate your legacy sources with modern tools and cloud platforms to improve the scalability and agility of your data warehouse. While some legacy components remain intact, other components are modernized, and you build on top of that to add new functionality using modern integration and automation tools.

The benefits of this approach are:

· Additional processing power and storage capacity can be added on-demand with a modern cloud platform, improving scalability and reducing costs for hardware upgrades.

· Allows you to have a more controlled environment for experimenting with the modern platforms and the cloud since you already have your existing data warehouse in place.

Key Drivers for Data Warehouse Modernization

Every business has its own reasons for modernizing its data warehouse, but some common drivers generally extend across all the initiatives. Let’s take a look at what these drivers are.

Business drivers

Some of the most critical drivers for data warehouse modernization are business-related and rightly so. The true ROI of a data warehouse is its ability to support better business decisions. Here is a list of the most common business drivers for modernizing data warehouses:

  • New business requirements: As a business expands, it needs more and more data to support its business goals. But, as a data warehouse ages, its ability to support new business requirements becomes limited. Ultimately, it becomes essential for businesses to move towards modernization to ensure that they keep receiving accurate, timely insights from their data. After all, why organizations need data warehouses in the first place is to support their reporting and analysis needs.
  • Better collaboration between IT and business users: Often, delays in reporting and analytics can be caused because your business team and IT team have separate, misaligned goals. Imagine a scenario where business users want to add a new KPI to one of its reports, but the IT team is too busy with the upkeep and maintenance of a legacy data warehouse. A modern data warehouse helps solve this by making it simpler for IT teams to work with the data warehouse, thus improving collaboration with business users and minimizing delays.
  • Support for self-service BI and analytics: Business Intelligence (BI) and analytics tools have evolved significantly in the past decades, making it easier for business users to generate the reports and insights they need directly from data. But, if you still have a legacy data warehouse at the backend, it can be difficult to match the speed and volume of data that business users expect and need for self-service BI. This happens because of several reasons, the most prominent of which are:
    • The lack of direct integration with self-service BI tools, requiring additional development time and effort to support your analytics needs.
    • ETL pipelines that run on a fixed schedule (such as once every day), which rules out the possibility of generating real-time reports and insights.
    • The lack of flexibility, making it difficult to adapt to new business requirements.
  • Pressure from the competition: With most organizations now looking to invest in modern technologies, modernization has now become more of a necessity rather than a luxury. Businesses want to give their competition a tough time with real-time insights for tuning sales and marketing campaigns, which legacy data warehouses are not designed to deliver.

Technological drivers

At the core of a data warehouse is an architecture comprising many moving parts that work like clockwork to drive data-driven processes. Any IT team working with the maintenance and upkeep of a legacy data warehouse knows how challenging it can be. Here are some of the most pressing technological drivers for DW modernization:

  • Support for modern platforms and systems: As a business, it is important to stay on top of technological advancements to make your processes more efficient and reliable. A great example of this is how far along zero-code ETL tools, self-service BI, and DW automation platforms have come in the last decade. From hand-coding data and reporting pipelines to automate almost every aspect of data warehousing leading to faster, accurate reporting and analytics solutions, modernizing your architecture opens pathways to integrating newer technologies for agile decision making. By adopting such modern platforms and systems, you can meet new business requirements without facing high development times and support costs.
  • Real-time data warehousing: We are now in the age of big data, where business decisions can and are taken in real-time. Speed is a critical technological driver for data warehouse modernization, and if you want access to real-time reports and self-serve BI, then you need to look towards increasing your capacity and capabilities to support real-time data warehousing.
  • Data integration: As businesses expand, data starts coming in from a great variety of sources, including SaaS tools, ERP systems, web applications, cloud databases, social media, and the list goes on. What you need is to consolidate all this data in your data warehouse in real-time to draw value from it. This is where the need for easy and quick data integration processes comes in, which is an area most legacy data warehouses lag.
  • New data types and platforms: Data now comes in all sizes and shapes (or should we say ‘types’). We have structured data, semi-structured data, and unstructured data (that is stored in plain text or JSON format) and newer platforms such as NoSQL. To be able to process and store all this data, businesses must extend their data warehouse to support such data types and platforms.
  • Adopting best practices and modern tools: As data warehouse technology evolves, so do the tools that we work with and the best practices for handling data. For instance, two decades ago, there were no DW builder tools, such as Astera DW builder, available that could support zero-code ETL and data warehouse automation. But now, we have a variety of such tools available that can make building and maintaining a modern data warehouse architecture much simpler and easier.

Strategic drivers

Strategic drivers are about being proactive and looking towards the future to ensure that your business has a scalable, cost-effective data warehouse. The goal here is to save time, money, and effort that is being spent on maintaining, upgrading, and upkeeping your data warehouse. Some of the important strategic drivers are:

  • Need for scalable, flexible architecture: Most legacy data warehouses were designed keeping a uniform, consistent reporting structure in mind. However, such an architecture is rigid towards incorporating meet new requirements. It also requires a lot of capital and preplanning to scale for accommodating growing volumes of data. As the need for scalability and flexibility grows, businesses need to look towards a modern data warehouse architecture that can expand and change as and when business requirements change.
  • Better security, privacy, and governance: Since the introduction of GDPR, businesses have been keen to improve data governance and take security initiatives to protect their data. One strategic driver for modernization is to adopt best security practices and adhere to modern standards and regulations to improve the security, privacy, and governance of your data warehouse. For example, modernizing your architecture gives you the opportunity to discover and document every aspect of your data, who used it and how, and all the processes it has been through before it reached its final destination. This enables you to ensure better governance and compliance with the provision for quick traceability if any problem arises.
  • Cost reduction: Modern data warehouse tools such as Astera DW Builder make it much simpler to build and maintain a data warehouse and reduce the costs in multiple ways. For starters, these user-friendly and intuitive tools give you the advantage of working with a single, unified platform that handles all the major aspects of building a modern data warehouse, such as data modeling, ETL code generation, metadata management, data quality, and profiling, and several others. In addition, they offer a no-code/low-code development environment to allow you to work with a small team of developers, saving costs in hiring additional resources. Lastly, such tools allow you to work in quick iterations, enabling your team to introduce changes to your data warehouse at the speed of business.

How Can You Achieve Data Warehouse Modernization?

Now that you know why data warehouse modernization is important let’s talk about how you can work towards achieving it.

1. Data integration

One of the first steps towards the modern data warehouse is to collect and combine all your data in real-time through data integration pipelines. You can achieve this by using modern data warehouse tools such as Astera DW Builder, which makes it straightforward to build complex integration pipelines in a matter of hours. With simple drag-and-drop actions, you can point to your data sources, configure the connection, apply any transformations you need, execute the ETL pipeline, all without writing a single line of code. Sounds simple, doesn’t it? It is.

2. Data modeling tools

Changing business data requirements directly equate to a change in the data models being used for the data warehouse. Do you need a new KPI? No problem, you can create a new data model or modify an existing one and redeploy it to cater to the new piece of information you need.

For this, you need to replace your previous legacy process with a single zero-code data warehousing tool that can perform all the basic and advanced data manipulation tasks such as moving, cleaning, and transforming data. Once these tools are integrated, making changes in a modern data warehouse are as simple as updating your data models to reflect the new data in your reports or dashboards. No code, no scripts involved.

3. Data warehouse automation

Modernization is not just a one-time effort but an ongoing practice, and therefore, it is important to automate as many processes as you can. You want to work with an agile architecture with a turnaround time of hours or days instead of months to enable you to integrate the latest technologies and platforms to desired results. This is the reason DW automation should be considered as a key aspect to drive your modernization efforts.

With a data warehouse automation solution, such as Astera DW Builder, you can opt for a metadata-driven approach to streamline and automate dimensional modeling, ETL/ELT processes, cloud deployment, and more, without having to update your entire data warehouse time and time again.

A Game-Changing Solution for Data Warehouse Modernization

The benefits of the modern data warehouse are immense, and you don’t want to wait until your legacy systems give in and stop delivering value before you take the modernization initiative. If you are looking for a reliable and powerful solution for data warehouse modernization, Astera DW Builder is the answer.

Astera DW Builder is an all-in-one data warehouse suite that uses a metadata-driven architecture to help build and manage your data warehouse. Whether you want to take your enterprise data architecture to Snowflake, Microsoft Azure, Oracle, or Amazon Redshift, you can rely on the platform to modernize your data warehouse into the desired destination. You can perform dimensional data modeling, bring data with 40+ sources, build ETL pipelines, generate target-platform native code, apply 600+ transformations, and perform data warehouse automation, all through a single platform.

Astera DW Builder lies at the very heart of the modern data warehouse, looking after all the low-level development for you so your IT team can focus on the outputs (i.e., high-quality insights and reports) rather than the process. Explore how you can ensure the success of your data warehouse modernization project with Astera DW Builder by scheduling a demo with our product experts today.