How a Data Lakehouse Architecture can Modernize Data Lakes

11:59 pm
July 5, 2023

Data lakes have been utilized by large corporations for over a decade to support their analytic operations. However, many of these deployments have become overwhelmed with data, leading to what some describe as “data swamps.” Despite this, data lakes still hold a significant amount of valuable data that is difficult to move, migrate, or modernize.

The Challenges of a Monolithic Data Lake Architecture

Data lakes are repositories of data at scale, where data can be stored in its raw form or optimized for specialized engines. However, the introduction of Spark as a processing framework for big data posed challenges within existing data lake environments, often requiring additional compute clusters dedicated to running Spark. Additionally, data governance and the ability to track the origin, ingestion, and transformation of data remains an unexplored frontier.

Why Modernize Your Data Lake?

There is an urgent need to modernize data lake deployments in order to protect the investment in infrastructure, skills, and data. To address this, the industry has looked at existing data platform technologies and identified key features that should be incorporated, including resilient and scalable storage, open data formats, open metadata, data update capabilities, and comprehensive data security and governance. This has led to the emergence of the data lakehouse, a unified and cohesive data management solution that combines the strengths of data warehouses and data lakes.

Benefits of Modernizing Data Lakes with Watsonx.data

IBM has developed watsonx.data as a solution for modernizing data lakes and data warehouses without the need for migration. It is a hybrid data store that can be run on customer-managed infrastructure and in the cloud, and it is built on a lakehouse architecture. Watsonx.data leverages open-source components and offers interoperability, co-existence, and metadata exchange. It enables companies to protect their existing investments in data lakes and warehouses, expand their installations, and gradually modernize their data management strategy. One key advantage is the multi-engine strategy, which allows users to leverage different technologies for different tasks, leading to cost savings in data management and processing.

What’s Next?

If your goal is to evolve and modernize your data management strategy towards a hybrid analytics cloud architecture, IBM’s watsonx.data deserves your consideration. It offers a data lakehouse architecture that can protect your existing investments and provide a unified data platform for all your data management needs.

Frequently Asked Questions (FAQ)

What is a data lakehouse architecture?

A data lakehouse architecture is a data platform that combines the strengths of data warehouses and data lakes into a unified and cohesive data management solution. It incorporates features such as resilient and scalable storage, open data formats, open metadata, data update capabilities, and comprehensive data security and governance.

Why should I modernize my data lake?

Modernizing your data lake is important to protect your investment in infrastructure, skills, and data. It allows you to expand and improve your data management capabilities, address challenges such as data governance and performance, and leverage the benefits of a data lakehouse architecture.

What are the benefits of modernizing data lakes with watsonx.data?

Watsonx.data, developed by IBM, offers a solution for modernizing data lakes and data warehouses without the need for migration. It allows you to protect your existing investments, expand your installations, and gradually modernize your data management strategy. It offers a multi-engine strategy, cost savings in data management and processing, and a unified data platform for all your data management needs.

How can I get started with watsonx.data?

You can explore the watsonx.data solution brief and visit the watsonx.data product page to learn more about the features and functionalities of this data lakehouse architecture solution offered by IBM.

Can watsonx.data be run on-premises and in the cloud?

Yes, watsonx.data can be run on customer-managed infrastructure (on-premises and/or IaaS) as well as in the cloud. Its hybrid nature allows for flexibility in deployment options.

Does watsonx.data support different data processing technologies?

Yes, watsonx.data supports a multi-engine strategy, which means it allows users to leverage different data processing technologies, such as Presto and Spark, for different tasks. This flexibility enables users to choose the right technology for the right job at the right time.

Does modernizing data lakes with watsonx.data require data migration?

No, watsonx.data minimizes the need for data migration and application migration by offering a choice of compute. This makes the process of modernizing existing data lake deployments easier and more efficient.


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