Five Innovative Approaches To a Modern Data Platform
In this article we cover all the best practices to building a modern data platform on the cloud.
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The Dremio Blog
Cloud Data Lakes - What You Need to Know
In this article we show you everything you need to know to move your data to the cloud. Options, advantages, and much more.
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Announcing Dremio 3.3
Dremio 3.3 includes many key features that continue to enhance the performance, security and administration of Dremio, providing faster time to insight and ease of access to data - see the highlights.
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Why there isn’t an Apache Arrow article in Wikipedia
In fact, the article has been submitted 4 times, not just by me but also by others, and declined each and every time.
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Modern Data Platform and the Cloud
This is the second article in the series of building a Modern Data Platform.
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Four Key Elements of a Successful Data Lake
Data lakes are an agile, low-cost way for companies to store their data, but without the right tools, the data lake can grow stagnant and become a data swamp.
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Characteristics, Whats, and Whys of the Modern Data Platform
If you Google 'modern data platform' you will get a lot of advertisement. Let’s try to agree on what modern data platform is.
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It’s Time to Replace ODBC & JDBC
ODBC and JDBC were invented 27 years ago, Apache arrow arrived to bring the best-in-class performance in the big data world.
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What is ADLS Gen2 - and why it matters
The second generation of ADLS, also known as ADLS Gen2, brings together all the great features of ADLS Gen1 and Azure Blob Storage.
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Announcing Dremio 3.2
Dremio 3.2 includes over 200 improvements, including support for ADLS Gen2, big speed improvements on S3 and ADLS via predictive pipelining, and support for Kubernetes and Helm deployments - see the highlights.
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Success Story - How Hotmart uses Dremio to gain insights from data, faster.
How Hotmart uses Dremio to gain insights from data, faster.
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Announcing Dremio University
Dremio Launches Free Online Training Courses for Data Engineers, Analysts, and Data Scientists.
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Announcing Dremio 3.1
Dremio 3.1 includes many new features and performance improvements - see the highlights.
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Announcing Dremio 3.0
Dremio 3.0 is a major release that includes many new features, performance improvements and security enhancements - see the highlights.
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Adding a User Defined Function to Gandiva
Learn how to add User Defined Functions to Gandiva.
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Gandiva Initiative Update: Improving SQL Projection Performance by 70x
Exploring performance improvements for SQL processing in Dremio based on Gandiva Initiative for Apache Arrow.
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Introducing the Gandiva Initiative for Apache Arrow
In-depth technical description of Dremio's Gandiva Initiative for Apache Arrow.
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The Origin & History of Apache Arrow
A background and overview of the Apache Arrow project from the PMC Chair, Jacques Nadeau.
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Introducing the Dremio Data Science Index
Read more about the methodology behind our Data Science Index – which tracks the popularity of data science tools.
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Announcing Dremio 2.0 – Starflake Reflections, REST APIs, and more!
Dremio 2.0 is a major release that includes many new features, performance improvements, and stability enhancements - see the highlights.
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Introduction to Starflake Data Reflections
Behind the scenes, invisible to end users, a relational cache comprising data materializations, also known as Data Reflections™, enables Dremio to accelerate queries from users and tools.
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The Winter Olympics Story: How I Did It
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Trump Twitter Sentiment Analysis: How I Did It
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Java Vector Enhancements for Apache Arrow 0.8.0
Technical performance review of enhancements to Java vectors in Apache Arrow 0.8.0
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Looking Back At How We Exited Dremio From Stealth
Our CEO reflects on two years of stealth and exiting Dremio from stealth.
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Recognizing A New Tier
Dremio's co-founder describes his vision for starting the company and the future of data analytics.
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Introducing Apache Arrow: Columnar In-Memory Analytics
Apache Arrow establishes a de-facto standard for columnar in-memory analytics which will redefine the performance and interoperability of most Big Data technologies.
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Tuning Parquet file performance
A brief discussion about how changing the size of a Parquet file’s ‘row group’ to match a file system’s block size can effect the efficiency of read and write performance.
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