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Improved Ascend for Databricks, New Lineage Visualization, and Better Incremental Data Ingestion

Ascend.io

Improved Support for Databricks To highlight our improved Databricks capabilities, our re:Invent booth was next to theirs, and we chose to power our demos with their Lakehouse. More and more customers are dramatically accelerating their time to value with Databricks data pipelines by leveraging Ascend automation.

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Introducing Vector Search on Rockset: How to run semantic search with OpenAI and Rockset

Rockset

To highlight these new capabilities, we built a search demo using OpenAI to create embeddings for Amazon product descriptions and Rockset to generate relevant search results. In the demo, you’ll see how Rockset delivers search results in 15 milliseconds over thousands of documents.

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Data Pipeline Observability: A Model For Data Engineers

Databand.ai

Having a bigger and more specialized data team can help, but it can hurt if those team members don’t coordinate. More people accessing the data and running their own pipelines and their own transformations causes errors and impacts data stability. Want to learn more about how Databand can help you manage data pipelines?

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8 Data Ingestion Tools (Quick Reference Guide)

Monte Carlo

At the heart of every data-driven decision is a deceptively simple question: How do you get the right data to the right place at the right time? The growing field of data ingestion tools offers a range of answers, each with implications to ponder. Fivetran Image courtesy of Fivetran.

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Next Stop – Predicting on Data with Cloudera Machine Learning

Cloudera

This integration is key in assuring that models evolve with the data – to avoid, for example, model drift. Thus, successful ML initiatives not only depend on the ability to quickly productionize models but they also depend on seamless access to data to train (and re-train) those models. Final Words. Additional Resources.

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Machine Learning with Python, Jupyter, KSQL and TensorFlow

Confluent

It allows real-time data ingestion, processing, model deployment and monitoring in a reliable and scalable way. This blog post focuses on how the Kafka ecosystem can help solve the impedance mismatch between data scientists, data engineers and production engineers. integration) and preprocessing need to run at scale.

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Data Freshness Explained: Making Data Consumers Wildly Happy

Monte Carlo

Identify the business owners of those data assets. In other words, who will be most impacted by a data freshness or other data quality issue? Ask them how they use their data and how frequently they access it. Create a SLA that specifies how frequently and when the data asset will be refreshed.