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High Performance SQL: AWS Graviton2 Benchmarks with Presto and Treasure Data CDP Last updated March 27, 2020 In December, AWS announced new Amazon EC2 M6g, C6g, and R6g instance types powered by Arm-based AWS Graviton2 processors. It is the second Arm-based processor designed by AWS following the first AWS Graviton processor introduced in 2018. Graviton2-based M6g instances deliver up to 40 percen

Learn about how we protect our customers’ data as a “data processor” or “service provider”: Data Processing Addendum Data Processing Addendum FAQ Sub-processors’ List & Subscription to Notifications Treasure Data’s Data Privacy Framework Privacy Policy Government Data Request Policy Transparency Report Data Security Learn about how we handle and protect personal data as a “data controller”: Treasu

Redshift is 400x Bigger than MySQL Yet MySQL is More Popular Last updated October 8, 2015 The Amazon Redshift COPY Command Guide is now available! There are good reasons for the hype around Amazon Redshift. Redshift is blazing fast and not that much more expensive than MySQL or PostgreSQL, the traditional mainstay of data engineers. But is Amazon Redshift really becoming predominant in the world o

By Hiro Yoshikawa, CEO and co-founder, Treasure Data Today all of us at Treasure Data enter a new phase in our history after being acquired by global chip technology and IoT services leader company Arm. It is a time of immense opportunity – as we’ll gain from the investment power of being part of Arm. You can find many details here, but I wanted to take a moment to discuss what this will mean for

Press Release Treasure Data Launches Treasure Workflow to Better Manage Data Workflow for Enterprises with Multi-Cloud Infrastructure Last updated November 29, 2016 Creator of First-Ever Live Data Management Platform Now Allows Teams to Collect, Transport, and Analyze Data Seamlessly Across Multiple-Vendor Cloud Infrastructures from AWS, Google, Microsoft, and More MOUNTAIN VIEW, CALIF. (PRWEB) NO

Distributed Logging Architecture in the Container Era Last updated August 3, 2016 TL;DR: Containers and Microservices are great, but they cause big problems with logging. You should do what Docker does: Use Fluentd. Also, if you need scale and stability, we offer Fluentd Enterprise. Microservices and Macroproblems Modern tech enterprise is all about microservices and, increasingly, containers. Mic

Routing Data from Docker to Prometheus Server via Fluentd Last updated July 19, 2016 See the video of the full integration here:  https://www.youtube.com/watch?v=uyu-GeAM-xk&feature=youtu.be Possibly the best way to build an economy of scale around your framework, whatever it is, is to build up your library of integrations – or integrators – and see what and who your new partners can bring into th

Fluentd, Kubernetes and Google Cloud Platform – A Few Recipes for Streaming Logging Last updated June 14, 2016 Maybe you already know about Fluentd’s unified logging layer. Maybe you are already familiar with the idea that logs are streams, not files, thus it’s necessary to think of a logging layer dynamically this way. Actually, it’s that very last point that lends a crucial understanding to how

Percentage of times these skills showed up in data-related job descriptions Keep in mind that most job ads (and recruiters) are behind the curve on invoking new technology, (where’s Fluentd?!?!  What about ELK Stack?) because recruiters and HR typically get their information second hand.  Plus, cutting-edge technology typically enjoys a period of minimal adoption while it is being tried out. If yo

Build a Simple Recommendation Engine with Hivemall and Minhash Last updated February 16, 2016 This is a translation of this blog post, printed with permission from the author. In this post, I will introduce a technique called Minhash that is bundled in Treasure Data’s Hivemall machine learning library. Minhash is not usually thought of as a machine learning technique, but as you will see in this p

Making Magic with pandas-td Last updated August 11, 2015 Magic functions enable common tasks by saving you typing. (NOTE:  Pandas itself doesn’t have magic functions; the IPython kernel does.) Magic functions are functions preceeded by a % symbol. Magic functions have been introduced into pandas-td version 0.8.0!  Toru Takahashi from Treasure Data walks us through. Treasure Data’s magic functions

5 Tips to Optimize Fluentd Performance Last updated August 18, 2015 We’ve recently gotten quite a few questions about how to optimize Fluentd performance when there is an extremely high volume of incoming logs. Kazuki Ohta presents 5 tips to optimize fluentd performance. They are: Use td-agent2, not td-agent1. Use ‘num_threads’ option. Avoid extra computations. Use external ‘gzip’ command for TD/S

Collecting All Docker Logs with Fluentd Last updated July 7, 2015 Logging in the Age of Docker and Containers Just in case you have been offline for the last two years, Docker is an open platform for distributed apps for developers and sysadmins. By turning your software into containers, Docker lets cross-functional teams ship and run apps across platforms seamlessly. If you are interested in depl

Data Science 101: Interactive Analysis with Jupyter, Pandas and Treasure Data Last updated June 23, 2015 In case you were wondering, the next time you overhear a data scientist talking excitedly about “Pandas on Jupyter”, s/he’s not citing the latest 2-bit sci-fi from the orthographically challenged! Treasure Data gives you a cloud-based analytics infrastructure accessible via SQL.  Our interactiv

How to Get More Clicks for Digital Advertising: Step by Step Guide to Optimizing CTRs with Real-time Data + Machine Learning Last updated October 13, 2014 In the digital advertising space, optimizing the CTR (Click Through Rate) is one of the major challenges to increasing the performance of the advertising networks. Often times, machine learning algorithms are used to optimize what ads are releva

Presto versus Hive: What You Need to Know Last updated March 20, 2015 There is much discussion in the industry about analytic engines and, specifically, which engines best meet various analytic needs. This post looks at two popular engines, Hive and Presto, and assesses the best uses for each. How Hive Works Hive translates SQL queries into multiple stages of MapReduce and it is powerful enough to

Treasure Data Joins the Linux Foundation Last updated March 24, 2015 Today is a big step forward for our customers and community in general, as we officially join the Linux Foundation. As you may know, our company is driven by an open source culture: We believe that continuous innovation, integration and knowledge sharing makes it possible to solve real-world problems. At Treasure Data, we are alw

Four Reasons Presto is the Best SQL-on-Hadoop (That You Haven’t Heard Of) Last updated March 12, 2015 Presto is an in-memory distributed SQL query engine developed by Facebook that has been open-sourced since November 2013. Presto has a number of key advantages over other SQL-on-Hadoop engines, yet these benefits are not widely recognized or understood. Reason #1: Presto is Plenty Fast Unlike MapR

Managing the Data Pipeline with Git + Luigi Last updated February 25, 2015 One of the common pains of managing data, especially for larger companies, is that a lot of data gets dirty (which you may or may not even notice!) and becomes scattered around everywhere. Many ad hoc scripts are running in different places, these scripts silently generate dirty data. Further, if and when a script results i