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The 2016 Crystal Ball – What’s Next in Data?

Alation

Considering what we’ve seen this year in industry trends and patterns, we have compiled some predictions for 2016 from our co-founders at Alation. Venky Ganti, CTO & Co-Founder: Data sprawl will finally hit its threshold. Data sprawl has been prevalent for several years. 2016 will be the year of the “logical data warehouse.”

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Powering the future: The synergy of IBM and AWS partnership

IBM Journey to AI blog

We are in the midst of an AI revolution where organizations are seeking to leverage data for business transformation and harness generative AI and foundation models to boost productivity, innovate, enhance customer experiences, and gain a competitive edge. Watsonx.data on AWS: Imagine having the power of data at your fingertips.

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Why Open Table Format Architecture is Essential for Modern Data Systems

phData

Versioning also ensures a safer experimentation environment, where data scientists can test new models or hypotheses on historical data snapshots without impacting live data. Note : Cloud Data warehouses like Snowflake and Big Query already have a default time travel feature. FAQs What is a Data Lakehouse?

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Celebrating 40 years of Db2: Running the world’s mission critical workloads

IBM Journey to AI blog

Db2 Warehouse SaaS, on the other hand, is a fully managed elastic cloud data warehouse with our columnar technology. watsonx.data integration At Think, IBM announced watsonx.data as a new open, hybrid and governed data store optimized for all data, analytics, and AI workloads.

Database 101
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10 Years Later: Who’s the GOAT of Data Catalogs?

Alation

March 2015: Alation emerges from stealth mode to launch the first official data catalog to empower people in enterprises to easily find, understand, govern and use data for informed decision making that supports the business. April 2016: Tesco Group becomes first customer outside North America. What do we mean by everything ?

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Gartner Data & Analytics London: Human Curation + Machine Learning

Alation

By leveraging Google-like smart search to find data assets; using automation and self-learning instead of burdening people with the need to manually update metadata in multiple places; and ensuring that metadata is maintained by the whole data community and is not dependent on a centralized IT team.

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Extract non-PHI data from Amazon HealthLake, reduce complexity, and increase cost efficiency with Amazon Athena and Amazon SageMaker Canvas

AWS Machine Learning Blog

In this post, we used Amazon S3 as the input data source for SageMaker Canvas. However, we can also import data into SageMaker Canvas directly from Amazon RedShift and Snowflake—popular enterprise data warehouse services used by many customers to organize their data and popular third-party solutions.

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