Simplifying Data Management through Metadata Integrations and AI Infusion – Kevin Shen, IBM

Simplifying Data Management through Metadata Integrations and AI Infusion – Kevin Shen, IBM

In this demo we’ll go through two key pieces of watsonx.data, IBM’s new Data Lakehouse offering. Multiple analytics engines working on the same data: – Demo: Multiple engines working on the same data set so you can use the analytics tools you love without having to deal with the ugly plumbing Semantic Automation: Leverage AI to simplify data discovery and manipulation, allowing your data to work for you – Demo: Using a chat interface to find tables of relevance and how AI can enrich data sets with semantic information

Building Modern Data Lakes for Analytics Using Object Storage – Satish Ramakrishnan, MinIO

Building Modern Data Lakes for Analytics Using Object Storage – Satish Ramakrishnan, MinIO

The modern data lake is distributed, unstructured and demands performance and scale – or better stated, performance at scale. Modern object stores are the ideal platform to pair with MPP query engines like Presto – particularly as the scale reaches tens or hundreds of petabytes with tens to hundreds of concurrent queries. In this talk, Satish Ramakrishnan will outline the better together attributes of the two technologies with a focus on the most sophisticated modern object storage features – from throughput optimizations, multi-cloud capabilities, cross-cloud active active replication and lifecycle management. Participants will come away with a reference architecture suited to query processing at object scale.

A Git-like Repository for your Data Lake – Vinodhini Sivakami Duraisamy, Treeverse

A Git-like Repository for your Data Lake – Vinodhini Sivakami Duraisamy, Treeverse

A Git-like Repository for your Data Lake – Vinodhini Sivakami Duraisamy, Treeverse We tend to adopt practices that improve the flexibility of development and the velocity of code deployment, but how confident are we that the complex data system is safe once it arrives in production? We must be able to experiment in production and automate actions while minimizing customer pain and reducing damage to code and data. If your product’s value is derived from data in the shape of analytics or machine learning, losing it, or having corrupted data, can easily translate into pain. In this session, you will discover how chaos engineering principles apply to distributed data systems and the tools that enable us to make our data workloads more resilient. 

Build & Query Secure S3 Data Lakes with Ahana Cloud and AWS Lake Formation

Build & Query Secure S3 Data Lakes with Ahana Cloud and AWS Lake Formation

AWS Lake Formation is a service that allows data platform users to set up a secure data lake in days. Creating a data lake with Presto and AWS Lake Formation is as simple as defining data sources and what data access and security policies you want to apply. In this talk, Wen will walk through the recently announced AWS Lake Formation and Ahana integration