Remove Cloud Storage Remove Data Ingestion Remove Data Lake
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Top Data Lake Vendors (Quick Reference Guide)

Monte Carlo

Data lakes are useful, flexible data storage repositories that enable many types of data to be stored in its rawest state. Traditionally, after being stored in a data lake, raw data was then often moved to various destinations like a data warehouse for further processing, analysis, and consumption.

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Why Open Table Format Architecture is Essential for Modern Data Systems

phData: Data Engineering

Note : Cloud Data warehouses like Snowflake and Big Query already have a default time travel feature. However, this feature becomes an absolute must-have if you are operating your analytics on top of your data lake or lakehouse. It can also be integrated into major data platforms like Snowflake.

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The No-Panic Guide to Building a Data Engineering Pipeline That Actually Scales

Monte Carlo

At the front end, you’ve got your data ingestion layer —the workhorse that pulls in data from everywhere it lives. Gone are the days of just dumping everything into a single database; modern data architectures typically use a combination of data lakes and warehouses.

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Real-Time Data Ingestion: Snowflake, Snowpipe and Rockset

Rockset

Although Snowflake is great at querying massive amounts of data, the database still needs to ingest this data. Data ingestion must be performant to handle large amounts of data. Without performant data ingestion, you run the risk of querying outdated values and returning irrelevant analytics.

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Discover And De-Clutter Your Unstructured Data With Aparavi

Data Engineering Podcast

Acryl Data provides DataHub as an easy to consume SaaS product which has been adopted by several companies. Signup for the SaaS product at dataengineeringpodcast.com/acryl RudderStack helps you build a customer data platform on your warehouse or data lake. What are the mechanisms that you use for categorizing data assets?

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Most important Data Engineering Concepts and Tools for Data Scientists

DareData

Our goal is to help data scientists better manage their models deployments or work more effectively with their data engineering counterparts, ensuring their models are deployed and maintained in a robust and reliable way. DigDag: An open-source orchestrator for data engineering workflows.

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Cloudera Data Platform extends Hybrid Cloud vision support by supporting Google Cloud

Cloudera

With the addition of Google Cloud, we deliver on our vision of providing a hybrid and multi-cloud architecture to support our customer’s analytics needs regardless of deployment platform. . Data Preparation (Apache Spark and Apache Hive) . Google Cloud Storage buckets – in the same subregion as your subnets .