IBM watsonx.data integration

Score7.8 out of 10

22 Reviews and Ratings

What is IBM watsonx.data integration?

IBM watsonx.data integration works across all integration styles, data types and storage architectures to make pipeline design and optimization durable, and data AI-ready.

Categories & Use Cases

Integration with MDM tools
Integration with master data management tools to ensure data consistency across the organization
Category average: 7.2
Simple transformations
Simple data transformations are calculations, data type conversions, aggregations and search and replace operations
Category average: 8.8
Integration with data quality tools
Integration with tools for cleansing, parsing and normalizing data according to business rules
Category average: 7.4

Connecto to Big Data and NoSQL
Ability to connect to non-traditional data sources like Hadoop and other big data technologies, and NoSQL databases
Category average: 7.6
Metadata management
Automated discovery of metadata with ability to synchronize and share metadata with other tools like Master Data Management
Category average: 7.5
Testing and debugging
Tool to debug and tune for optimal performance
Category average: 7.3

Pros

Consolidating data from diverse sources (databases, cloud storage, APIs)
Unifying data silos for organized reporting and consistency
Foundational data management and governance features

Cons

Complex, laggy, and overwhelming user interface
Challenging initial setup and integration processes
Lack of robust native connectors for legacy dataflows and third-party software

Yashkumar Patel

Clinical Data Workflow Specialist in Corporate at Cliantha Research (201-500 employees)

Use Cases and Deployment Scope

I use it for consolidating clinical trial data from multiple source systems to automate data feeding, transforming it and get standardized datasets for analytics and reports. It helps to find data quality issues, improving governance and better traceability. All the statistical workflows, reutilizing it over multiple studies which benefits us to get faster access to analysis ready data.

Pros

It integrates data from databases, cloud storages to work efficiently
It handles growing data volumes without requiring any effort for workflow updation.
It is fully traceable and has monitoring capabilities which helps track down data movement and troubleshoot issues.

Cons

Some workflows configurations can feel complex, especially when managing large projects.
more hands on examples might help new users to become more productive faster
Sometimes the performance seems laggy, especially working on bulk data.

Return on Investment

It has reduced manual effort with transformative workflows.
Governance, traceability and audit readiness has been improved.
We may need to improve performance optimization for highly bulk data volume.

Usability

Alternatives Considered

IBM Aspera on Cloud and Azure Databricks

Other Software Used

IBM Aspera on Cloud, Microsoft Excel

Pappi Reddy View profile

Senior Database Administrator in Information Technology at Peristent Systems (10,001+ employees)

Use Cases and Deployment Scope

The use case we use IBM watsonx.data is mainly for integrating multiple storgae sources of our clients to build AI use cases, we are building a central repository which can be used as context to build AI use cases. Client is in finance and insurance domain , there is lot of scattered data and data which makes less sense for humans but integrating it one place and driven lot of value

Pros

Pull / integrate data from multiple source platforms
monitoring capabilties are good , provides very good metrics
Removing sensitive data from sources
Does things in cost effective manner

Cons

Finding Human Expertise is difficult
Figuring out the issue with broken pipline could be time consuming
Integrations with Databases is possible but difficult
Handling large volumes efficiently

Return on Investment

Qurying large datasets has in significantly reduced time
Infra costs are lesser compared to its competitoers
Cross deparmental workflows can be easily integrated
Pipeline breaking is very minimal

Usability

Alternatives Considered

Snowflake and Azure Databricks

Other Software Used

Oracle Database, Oracle GoldenGate, MongoDB, SAP HANA Cloud

Verified User

Engineer in Information Technology (10,001+ employees)

Use Cases and Deployment Scope

We wanted a single tool through which we can orchestrate both extract/transform/load structured data as well a receive data from streaming for unstructured data while also being able to move data across hybrid IT environment for self service analytics. In addition, we also use the tool to replicate data for high availability.

Pros

ability to handle large data sets in hybrid multi cloud environment
optimizes storage so that the queries get executed with minimal latency
supports lakehouse architecture where both structured and unstructured data can be held in one place

Cons

initial set up should be through drag and drop connectors/integrations to cloud data lakehouse, ELT tools and on prem data repository

Return on Investment

We have been able to have a unified data lakehouse architecture for both structured and unstructured data in the cloud with integration to on prem data sources and this has enabled us to consolidate data and reduce costs while improving self service analytics

Usability

Alternatives Considered

AWS Glue

Verified User

Engineer in Information Technology (5001-10,000 employees)

Use Cases and Deployment Scope

IBM watsonx.data addresses the challenges of data integration across different applications varying in requirements and formats. The ability to unify data across various sources and provide orchestration to enable complex workflows is important.

Pros

Data Integration
Data Cleansing
Data Transformation

Cons

Could provide more integration with legacy dataflows
More documentation on data residency requirements

Return on Investment

Better data residency
Intuitive Data Orchestration

Usability

Alternatives Considered

Azure Databricks

Other Software Used

Azure Databricks, Azure AI Search, GitHub Copilot

Verified User

Director in Information Technology (1001-5000 employees)

Use Cases and Deployment Scope

We got IBM watsonx.data basically to fix data sprawl issue. We did not want to increase the storage and CPU costing. Our data was spread across ancient DBs and many cloud instances. We use IBM watsonx.data in our main service management , infra reporting and ops telemetry. It helps keeping data governance centralised

Pros

Inplace query federation
Unified Governance
Access Controls

Cons

In built 3rd party connectors
Error Diags

Return on Investment

Cost reduction in DW CPU spend

Usability

Other Software Used

Microsoft Power BI, Amazon S3 Glacier, IBM Cloud Object Storage

Informatica Cloud Data Integration

Maia by Matillion

AWS Glue

IBM watsonx.data integration

What is IBM watsonx.data integration?

Categories & Use Cases