Why optimize your warehouse with a data lakehouse strategy

We pointed out that warehouses, known for high-performance data processing for business intelligence, can quickly become expensive for new data and evolving workloads. We also made the case that query and reporting, provided by big data engines such as Presto, need to work with the Spark infrastructure framework to support advanced analytics and complex enterprise data decision-making. To do so, Presto and Spark need to readily work with existing and modern data warehouse infrastructures. Now, let’s chat about why data warehouse optimization is a key value of a data lakehouse strategy.

Value of data warehouse optimization

Since its introduction over a century ago, the gasoline-powered engine has remained largely unchanged. It’s simply been adapted over time to accommodate modern demands such as pollution controls, air conditioning and power steering.

Similarly, the relational database has been the foundation for data warehousing for as long as data warehousing has been around. Relational databases were adapted to accommodate the demands of new workloads, such as the data engineering tasks associated with structured and semi-structured data, and for building machine learning models.

Returning to the analogy, there have been significant changes to how we power cars. We now have gasoline-powered engines, battery electric vehicles (BEVs), and hybrid vehicles. An August 2021 Forbes article referenced a 2021 Department of Energy Argonne National Laboratory publication indicating, “Hybrid electric vehicles (think: Prius) had the lowest total 15-year per-mile cost of driving in the Small SUV category beating BEVs”.

Just as hybrid vehicles help their owners balance the initial purchase price and cost over time, enterprises are attempting to find a balance between high performance and cost-effectiveness for their data and analytics ecosystem. Essentially, they want to run the right workloads in the right environment without having to copy datasets excessively.

Optimizing your data lakehouse architecture

Fortunately, the IT landscape is changing thanks to a mix of cloud platforms, open source and traditional software vendors. The rise of cloud object storage has driven the cost of data storage down. Open-data file formats have evolved to support data sharing across multiple data engines, like Presto, Spark and others. Intelligent data caching is improving the performance of data lakehouse infrastructures.

All these innovations are being adapted by software vendors and accepted by their customers. So, what does this mean from a practical perspective? What can enterprises do different from what they are already doing today? Some use case examples will help. To effectively use raw data, it often needs to be curated within a data warehouse. Semi-structured data needs to be reformatted and transformed to be loaded into tables. And ML processes consume an abundance of capacity to build models.

Organizations running these workloads in their data warehouse environment today are paying a high run rate for engineering tasks that add no additional value or insight. Only the outputs from these data-driven models allow an organization to derive additional value. If organizations could execute these engineering tasks at a lower run rate in a data lakehouse while making the transformed data available to both the lakehouse and warehouse via open formats, they could deliver the same output value with low-cost processing.

Benefits of optimizing across your data warehouse and data lakehouse

Optimizing workloads across a data warehouse and a data lakehouse by sharing data using open formats can reduce costs and complexity. This helps organizations drive a better return on their data strategy and analytics investments while also helping to deliver better data governance and security.

And just as a hybrid car allows car owners to get greater value from their car investment, optimizing workloads across a data warehouse and data lakehouse will allow organizations to get greater value from their data analytics ecosystem.

Discover how you can optimize your data warehouse to scale analytics and artificial intelligence (AI) workloads with a data lakehouse strategy.

Source: ibm.com

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Why companies need to accelerate data warehousing solution modernization

Unexpected situations like the COVID-19 pandemic and the ongoing macroeconomic atmosphere are wake-up calls for companies worldwide to exponentially accelerate digital transformation. During the pandemic, when lockdowns and social-distancing restrictions transformed business operations, it quickly became apparent that digital innovation was vital to the survival of any organization.

The dependence on remote internet access for business, personal, and educational use elevated the data demand and boosted global data consumption. Additionally, the increase in online transactions and web traffic generated mountains of data. Enter the modernization of data warehousing solutions.

Companies realized that their legacy or enterprise data warehousing solutions could not manage the huge workload. Innovative organizations sought modern solutions to manage larger data capacities and attain secure storage solutions, helping them meet consumer demands. One of these advances included the accelerated adoption of modernized data warehousing technologies. Business success and the ability to remain competitive depended on it.

Why data warehousing is critical to a company’s success

Data warehousing is the secure electronic information storage by a company or organization. It creates a trove of historical data that can be retrieved, analyzed, and reported to provide insight or predictive analysis into an organization’s performance and operations.

Data warehousing solutions drive business efficiency, build future analysis and predictions, enhance productivity, and improve business success. These solutions categorize and convert data into readable dashboards that anyone in a company can analyze. Data is reported from one central repository, enabling management to draw more meaningful business insights and make faster, better decisions.

By running reports on historical data, a data warehouse can clarify what systems and processes are working and what methods need improvement. Data warehouse is the base architecture for artificial intelligence and machine learning (AI/ML) solutions as well.

Benefits of new data warehousing technology

Everything is data, regardless of whether it’s structured, semi-structured, or unstructured. Most of the enterprise or legacy data warehousing will support only structured data through relational database management system (RDBMS) databases. Companies require additional resources and people to process enterprise data. It is nearly impossible to achieve business efficiency and agility with legacy tools that create inefficiency and elevate costs.

Managing, storing, and processing data is critical to business efficiency and success. Modern data warehousing technology can handle all data forms. Significant developments in big data, cloud computing, and advanced analytics created the demand for the modern data warehouse.

Today’s data warehouses are different from antiquated single-stack warehouses. Instead of focusing primarily on data processing, as legacy or enterprise data warehouses did, the modern version is designed to store tremendous amounts of data from multiple sources in various formats and produce analysis to drive business decisions.

Data warehousing solutions

A superior solution for companies is the integration of existing on-premises data warehousing with data lakehouse solutions using data fabric and data mesh technology. Doing so creates a modern data warehousing solution for the long term.

A data lakehouse contains an organization’s data in a unstructured, structured, semi-structured form, which can be stored indefinitely for immediate or future use. This data is used by data scientists and engineers who study data to gain business insights. Data lake or data lakehouse storage costs are less expensive than a enterprise data warehouse. Further, data lakes and data lakehouse are less time-consuming to manage, which reduces operational costs. IBM has a next-generation data lakehouse solution to achieve these business situations.

Data fabric is the next-generation data analytics platform that solves advanced data security challenges through decentralized ownership. Typically, organizations have multiple data sources from different business lines that must be integrated for analytics. A data fabric architecture effectively unites disparate data sources and links them through centrally managed data sharing and governance guidelines.

Many enterprises seek a flexible, hybrid, and multi-cloud solution based on cloud providers. The data mesh solution pushes down the structured query language (SQL) queries to the related RDBMS or data lakehouse by managing the data catalog, giving users virtualized tables and data. In data mesh principles, it never stores business data locally, which is an advantage for a business. A successful data mesh solution will reduce a company’s capital and operational expenses.

IBM Cloud Pak for Data is an excellent example of a data fabric and data mesh solution for analytics. Cloud technology has emerged as the preferred platform for artificial intelligence (AI) capabilities, intelligent edge services, and advanced wireless connectivity and etc. Many companies will leverage a hybrid, multi-cloud strategy to improve business performance and success and thrive in the business world. 

Best practices for adopting data warehousing technology

Data warehouse modernization includes extending the infrastructure without compromising security. This allows companies to reap the advantages of new technologies, inducing speed and agility in data processes, meeting changing business requirements, and staying relevant in this age of big data. The growing variety and volume of current data make it essential for businesses to modernize their data warehouses to remain competitive in today’s market. Businesses need valuable insights and reports in real-time and enterprise or legacy data warehouses cannot keep pace with modern data demands.

Data warehouses are at an exciting point of evolution. With the global data warehousing market size estimated to grow at a compound grow over 250% in next 5 years, companies will rely on new data warehouse solutions and tools that make them easier to use than ever before.

Cutting-edge technology to keep up with constant changes

AI and other breakthrough technologies will propel organizations into the next decade. Data consumption and load will continue to grow and provoke companies to discover new ways to implement state-of-the-art data warehousing solutions. The prevalence of digital technologies and connected devices will help organizations remain afloat, an unimaginable feat 20 years ago.

Essential lessons arise from an organization’s efforts to optimize its enterprise or legacy data warehousing technology. One vital lesson is the importance of making specific changes to modernize technology, processes, and organizational operations to evolve. As the rate of change will only continue to increase, this knowledge—and the capability to accelerate modernization—will be critical going forward.

No matter where you are at data warehouse modernization today, IBM experts are here to help modernize the right approach to fit your needs. It’s time to get started with your data warehouse modernization journey.

Source: ibm.com

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