watsonx.data : Master your data , supercharge your insights
So it’s a Friday evening and I am back with yet another page from my diary .
Today I am going to discuss about watsonx.data .
Like all my posts this one is a reflection of my own viewpoints without getting influenced or biased by any organization or individual .
I started my career with Data and has managed multiple personas around Data for a very long time and hence anything that has a core or
indirect relation with Data mesmerizes me .
Today when I see the Data-Ops team , encompassing and spanning across geographies , getting the right data to the right hands at the right time in the right way through pipelines that work seamlessly , I can’t help wondering if anyone would believe the contexts and moments we had witnessed when we used to claim ourselves as Data nerds only to ingest a data file (of say 1 lakh records only ) extracted from one relational database into another through an export and import utility that entailed considering data formats , types , vendor , operating system , data compatibility and an array of other factors . The story does end here . We finally used to end ingesting the data after 2 days through lots of trial and error and then would hop on to the next ingestion and the saga of pains and excitement would continue and follow .
I am also sure that Data personas of yesteryears would vividly remember how data fetched from far flung regions would cause a latency and how the
dense query plan would give developers some idea about the optimal path the query engine would follow to access the data from the storage till
the result set of the query would be formed .
In the middle of all these were the fun and challenges where Data personas of Mainframes and Windows and Unix would sometimes brainstorm over lunch and firefight together to make sure the data from one server would flow into another server and then into another and finally would land up to the reporting database .
So , I can go on and on and on with Data but today I wanted to focus on watsonx.data .
Let’s roll forward at a great speed from my Data nerdy days to the era of Gen-AI . I know a lot of water has flown under all the bridges of the world
in between but we will talk about the interim events some other day .
For quite sometime I used to consider Db2 as my first love but of late my passion has been watsonx.data ( Db2 by the way can be found both inside
as well as outside watsonx.data so my passion has become three fold now ). Since middle of last year till date I would have heard at least 500 times
how the trio of watsonx.ai , watsonx.data and watsox.governance are helping enterprise customers with their Gen-AI asks . We all recognize the importance of data in AI and Generative AI. Simply put, Generative AI creates new content — such as text, videos, summaries, emails, or results — by learning patterns from existing data and being guided by input data. For the system to work efficiently, it needs to be trained with quality data, a process known as prompt engineering. The better the data, the better the training, and the better the output!
Now that we understand the significance of data layer in Gen-AI , it is very important to make this data layer adopt a standard framework so that it can be integrated . It is also important to make this layer hold trusted data because if you train the Gen-AI system with data that is not trusted then the output may be inaccurate and misleading .
Organizations adopting Gen-AI need
- Data from landscapes that are siloed and complex ( the data could be within disparate databases , datawarehouse , legacy systems etc. in both structured and unstructured formats )
- Data that can be trusted .
This data layer is what watsonx.data is all about . To incubate the data in the standard and structure it should be , it was brainstormed and found that while
- Data lakes are prevalent it was hard to efficiently scale.
- Data warehouses, especially in the cloud, are highly performant but are not the most cost effective.
And hence there was a need to built an architecture that needed to be price performant , open source and low-cost object storage . This resulted in watsonx.data having a data lakehouse architecture that would be both fast and cost efficient to unify governed data for AI.
To my purview IBM probably is the only organization that is making the data layer and sublayer in a Gen-AI workflow so conspicuous that the personas associated around to manage the Gen -AI system can truly comprehend the essence of the use cases in a very holistic fashion .
All these help to have the user a seamless and a very fulfilling experience .
The data lakehouse architecture at the core of watsonx.data is not a theoritical masterpiece . It has been solving myriads of use cases across industries already in the real world .
Calling something as datalakehose is very easy theoritically but providing the base framework to build an architecture on top of it is what watsonx.data has made possible . Your Gen-AI data use cases might all look very different from one another but when you peel off the layers of requirements around it you find the data lakehouse architecure that watsonx.data provides to be very appealing and fit for purpose .
I would strongly suggest to read the manuals and documentation of watsonx.data from the IBM site .
According to me what watsonx.data has made possible are :
1. Providing the base framework of a data lakehouse to the enterprise at large so that data irrespective of it’s location across geography and nature can then be used for a solution that leverages this underlying framework to unlock new data insights . This means one can access data of mutiple formats through a single point of entry and share a single copy of data across one’s organization and workloads, without needing to migrate or recatalog.
2. Having Presto as an integrated query engine, which sits on top of the underlying data storage architecture and fulfills requests for data
by optimizing the data retrieval process. Presto is a distributed query engine that supports querying across diverse sources, including both
structured relational databases and unstructured and semi-structured NoSQL data sources. Likewise, Presto supports many different data formats, such as ORC, Avro, Parquet, CSV, JSON, and more.
3. Make the data part of the Gen-AI evolution so conspicuous that using this data for RAG and other AI use cases then becomes more meaningful and holistic . An integrated vector database within watsonx.data does this magic . Try to find what this vector database is called and then comment in the section below :-).
4. Integration with databases and modern data stacks makes the enterprises to leverage upon the existing data investments .
Owing to all of the above factors watsonx.data is finding great use cases across industry , geography and platforms .
There’s more !! watsonx.data can be deployed across hyperscalers or on-premises environment in minutes.
That’s watsonx.data to me in a nutshell but I am sure you will find more when you do a deep dive to discover the pearls in this oyster .