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Company Announcement: Treasure Data officially part of Softbank Vision Fund 2; Welcome Back Founding Leadership Team Company Announcement: Treasure Data officially part of Softbank Vision Fund 2; Welcome Back Founding Leadership Team Last modified: July 13, 2021 Treasure Data officially part of Softbank Vision Fund 2*; Welcome Back Founding Leadership Team We are thrilled to announce that Treasure
High Performance SQL: AWS Graviton2 Benchmarks with Presto and Treasure Data CDP High Performance SQL: AWS Graviton2 Benchmarks with Presto and Treasure Data CDP Last modified: March 4, 2022 High Performance SQL: AWS Graviton2 Benchmarks with Presto and Treasure Data CDP In December, AWS announced new Amazon EC2 M6g, C6g, and R6g instance types powered by Arm-based AWS Graviton2 processors. It is
Enhance your Google BigQuery with Treasure Data Result Output Enhance your Google BigQuery with Treasure Data Result Output Last modified: August 15, 2019 Stay tuned for our video of this integration! Google BigQuery is the choice for many. It’s the go-to for interactive analysis of enormous datasets and can process billions of rows in seconds. With no infrastructure to manage and the need fo
Distributed Logging Architecture in the Container Era Last modified: December 6, 2019 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.
Routing Data from Docker to Prometheus Server via Fluentd Last modified: August 17, 2019 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 int
Fluentd, Kubernetes and Google Cloud Platform – A Few Recipes for Streaming Logging Fluentd, Kubernetes and Google Cloud Platform – A Few Recipes for Streaming Logging Last modified: July 7, 2020 Fluentd, Kubernetes and Google Cloud Platform – A Few Recipes for Streaming Logging Maybe you already know about Fluentd’s unified logging layer. Maybe you are already familiar with the idea that logs are
Redshift v. BigQuery: Similarities, Differences and the Serverless Future? Redshift v. BigQuery: Similarities, Differences and the Serverless Future? Last modified: August 17, 2019 Redshift v. BigQuery: Similarities, Differences and the Serverless Future? In broad strokes, both BigQuery and Redshift are cloud data warehousing services. Honestly, the similarities are greater than the differences, a
A Self-Study List for Data Engineers and Aspiring Data Architects A Self-Study List for Data Engineers and Aspiring Data Architects Last modified: August 18, 2019 A Self-Study List for Data Engineers and Aspiring Data Architects With the explosion of “Big Data” over the last few years, the need for people who know how to build and manage data-pipelines has grown. Unfortunately, supply has not kep
Build a Simple Recommendation Engine with Hivemall and Minhash Build a Simple Recommendation Engine with Hivemall and Minhash Last modified: August 18, 2019 Build a Simple Recommendation Engine with Hivemall and Minhash 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 Hivem
Making Magic with pandas-td Last modified: March 14, 2022 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 modified: May 16, 2019 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/S3
5 Use Cases Enabled by Docker 1.8’s Fluentd Logging Driver Last modified: May 16, 2019 Docker 1.8 Is Here with Fluentd If you are interested in deploying Fluentd + Kubernetes/Docker at scale, check out our Fluentd Enterprise offering. Docker 1.8 is coming soon! One of the major items in the 1.8 releases is its support for Fluentd as a Logging Driver. As the inventor of Fluentd, we are really excit
Collecting All Docker Logs with Fluentd Last modified: August 18, 2019 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
Data Science 101: Interactive Analysis with Jupyter, Pandas and Treasure Data Data Science 101: Interactive Analysis with Jupyter, Pandas and Treasure Data Last modified: August 18, 2019 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 gi
How to Get More Clicks for Digital Advertising: Step by Step Guide to Optimizing CTRs with Real-time Data + Machine Learning How to Get More Clicks for Digital Advertising: Step by Step Guide to Optimizing CTRs with Real-time Data + Machine Learning Last modified: July 28, 2021 How to Get More Clicks for Digital Advertising: Step by Step Guide to Optimizing CTRs with Real-time Data + Machine Learn
Presto versus Hive: What You Need to Know Last modified: August 17, 2019 Presto versus Hive: What You Need to Know 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 sta
Managing the Data Pipeline with Git + Luigi Last modified: August 17, 2019 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
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