watsonx.data: Hybrid Open Data Lakehouse Solution

Hybrid Open Data Lakehouse Solution

The global data storage market is projected to more than triple by 2032. As data proliferates across on-premises systems, clouds, applications, and locations, it can lead to increased data silos, elevated costs, and greater complexity in managing AI and data workloads.

To tackle the challenges of today’s intricate data environment and scale AI effectively, organizations can leverage an open data lakehouse architecture. This combines the high performance and usability of data warehouses with the flexibility and scalability of data lakes.

The IBM watsonx.data platform offers an open, hybrid, and governed data lakehouse designed specifically for data and AI workloads, helping you maximize the value of your data landscape.

  • Access all your data across hybrid cloud environments effortlessly: Connect to storage and analytics systems within minutes, and manage all your data through a single entry point, supported by a unified metadata layer that spans both cloud and on-premises environments.
  • Optimize for price performance by utilizing watsonx.data. It helps fine-tune your data workloads with a variety of specialized engines, including Presto C++, Presto, Spark, and integrated data warehouse engines, ensuring both efficiency and cost-effectiveness.
  • Prepare and manage data for AI by unifying, curating, and efficiently preparing it for the models and applications you select. Ensure your AI is powered by trusted and high-quality data.
  • Deploy seamlessly across any cloud or on-premises environment in just minutes, thanks to workload portability enabled by Red Hat OpenShift.
 
Infrastructure manager watsonx.data

Connect to data in minutes:

Connect existing data with new data in minutes, unlocking valuable and trusted insights without the expense and complexity of governing, duplicating, and moving data. Users can explore and transform data using familiar SQL. Watsonx.data also integrates with a comprehensive ecosystem of IBM and third-party technologies, simplifying the development and deployment of your analytics workloads while maximizing the value of your existing data investments. Available through SaaS on IBM Cloud and Amazon Web Services, or as containerized software, Watsonx.data allows for seamless deployment across any cloud or on-premises environment in minutes, thanks to workload portability via Red Hat OpenShift.

Share a single copy of data:

Openness promotes collaboration and enhances data integrity while addressing security risks by reducing the number of data copies needed for various users and tools. Fewer copies translate to less software, lower hardware requirements, and reduced storage costs. With watsonx.data, you can access all your data across both databases and data lakes. Share large volumes of data using open table formats such as Apache Iceberg, which are optimized for high-performance analytics and large-scale processing. It supports multiple open formats for analytic datasets, allowing different engines to access and share the same data simultaneously with tools like Parquet, Avro, and Apache ORC. Rely on watsonx.data to manage metadata across multiple query engines, utilizing a single copy of data for all analytics and AI workloads.

A user friendly integrated console

Optimize workloads with fit-for-purpose query engines

No single analytics engine can meet all the diverse demands of various analytics needs. Addressing this wide range of requirements necessitates the use of multiple analytics engines.

By optimizing your data warehouse workloads, you can potentially cut data warehouse costs by up to 50% through the use of cost-effective object storage and specialized query engines. For instance, Presto is tailored for business intelligence (BI) workloads, while Spark is designed for machine learning and data science (ML/DS) tasks. Both engines can automatically scale up or down based on your needs. With just a few clicks, you can easily integrate a new query engine to meet your specific price-performance criteria.

The IBM

Optimize workloads with fit-for-purpose query engines

Reliable and well-governed data is crucial for ensuring the accuracy and effectiveness of AI applications. Preparing data for AI often involves creating vectorized embeddings, which facilitate rapid, low-latency queries and enable the effective use of extensive enterprise data for generative AI and retrieval-augmented generation (RAG) at scale.

Watsonx.data integrates Milvus vector databases, allowing you to store and query vectorized embeddings specifically for RAG scenarios. This integration ensures that AI applications are based on trustworthy data, enhancing both the relevance and accuracy of the results.

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When managing data for analytics and AI, many enterprises rely on a mix of data warehouses and data lakes, both on-premises and in the cloud. Discover how Watsonx.data helps you accelerate time to trusted insights with its built-in governance, access controls, and security features.

Curious about starting with a lakehouse architecture? Contact us today to learn how we can support your data and AI initiatives.

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