Forward thinking businesses see the value and potential that multicloud adoption offers. The only question is, how do you ensure effective ways of breaking down data silos and bringing data together for self-service access? It starts by modernizing your data integration capabilities – ensuring disparate data sources and cloud environments can come together to deliver data in real time and fuel AI initiatives.

A data fabric architecture can help – it requires strong data integration capabilities facilitating governed data access blending the right delivery pattern to match the use case. Whether it be batch (ETL or ELT), virtualization, replication, data preparation, real-time or event driven, you need flexible and augmented data pipelines to create and deliver data processes across your organization.

Recently, IBM was named a Leader in the 2022 Gartner® Magic Quadrant™ for Data Integration Tools, and though the data landscape is constantly shifting and evolving, IBM has been a consistent Leader in the report for 17 years.

IBM’s data integration capabilities help organizations implement a data fabric by integrating data across any cloud and giving clients options for:

• Portable integration
• Remote runtime data integration as-a-service execution capabilities for on-premises and multi-cloud execution
• Multi-directional data movement topology with high volume and low-latency integration
• Support for data governance
• Metadata exchange with third party metadata management and governance tools

Unlock Insights with IBM’s data integration tools

Let’s dive deeper into IBM’s suite of data integration tools and how we help empower organizations to unlock insights from data wherever it resides with security, governance and performance built within.

IBM DataStage allows for batch-style flexible data integration for all types of data on-premises and in the cloud. In addition to ETL/ELT and virtualization, when deployed along side tools like IBM Watson Knowledge Catalog, IBM Watson Query and IBM Infosphere Data Replication companies can achieve near real-time data syncs using easy to find shared data, that is secure and can be queried without moving or replicating. All of these tools are readily accessible on IBM Cloud Pak for Data as a Service, and are core to the platform’s data fabric architecture.

Modernize your legacy solutions

For customers looking to modernize and migrate legacy data integration tools to the cloud, IBM offers rich comprehensive tools that help customers understand their data landscape and how to migrate. IBM also offers modernization workshops to assist customers with evaluating and re-designing their data architecture.

The path forward with IBM and data integration 

Furthermore, we will continue to grow and optimize our data integration capabilities for the needs of the market. With IBM’s recent acquisition of Databand.ai, we’re addressing new use cases for customers that no other solution in the market can deliver, including data observability. Data observability allows data engineers to have visibility into data pipeline issues by automatically detecting anomalies based on historical execution patterns. The value of Databand.ai is that it enables incident process management as pipelines run, allowing for alerts and workflows to trigger as soon as issues are detected instead of waiting for a post run validation process to certify the result. With the acquisition of Databand.ai, IBM has a full spectrum observability offering, between APM, Data, and ML observability.

At IBM our data integration strategy remains clear – provide clients with the appropriate integration style executing where the data resides.

We’re excited to be named a Leader in The 2022 Gartner® Magic Quadrant™ for Data Integration Tools. Read the report for more information on our positioning and if you want to jump in and try some of our data integration capabilities, access the free trial today.

Gartner, Magic Quadrant for Data Integration Tools, By Ehtisham Zaidi, Sharat Menon, Robert Thanaraj, Nina Showell, 17 August 2022

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