Integrations - Treasure Data Blog - Part 3

Integrations Category

Serverless Data Integration for Data Analysts with Zapier

If you’re familiar with IFTTT.com and the simple If This Then That task automation recipes, then you already also have a pretty good idea how Zapier’s trigger-response mechanism works: you perform a simple ...

Supercharge your Salesforce Data Search Efforts with Treasure Data and Elasticsearch

Elasticsearch benefits not just those looking for text search, but those looking for instant insights and business intelligence from their textual and unstructured data. You might see Elasticsearch implemented ...

Capture your Kinesis Stream Analytics Events in Treasure Data

With data-driven companies like Coinbase, Dash and Supercell all getting on board with Amazon Kinesis for real-time delivery of in-app data, handling billions of events per day, we can imagine why you too might...

Wrangling Huge .CSVs Made Simple (with a little help from SQL)

Let's say you have a very large .csv (containing more than 80k rows) that you want to analyze and visualize. Usually, given only Excel and Tableau (or Looker, Chartio, etc.), you would have one of two options: Put it into Excel and run pivot tables, charts, and whatnot, OR Ingest the data di...

Importing and Exporting Google Spreadsheets with Treasure Data

Despite various business intelligence vendors’ campaigns against Excel and similar programs, spreadsheets continue to be the most popular business intelligence tool. In fact, they are more than a BI tool: Bec...

How to Succeed at Importing XML to Just About Anywhere (without converting to JSON first)

JSON is everywhere. Nowadays, almost any emerging data ingestion framework or REST API endpoint uses JSON as a data transfer format. In fact, many will argue that since data is stored in records and array...

February 3, 2016

Bringing Machine Learning to Elasticsearch with Logz.io and Treasure Data

As organizations build up their data infrastructure, we often see different, divergent pipelines sprouting up to accommodate respective needs. This leads to data silos as well as analytics silos: one team builds its own data pipeline for themselves......

August 18, 2015

5 Tips to Optimize Fluentd Performance

We’ve rewritten the Ruby supporting MessagePack, the highly efficient binary serialization format used internally. (MessagePack was invented by TD‘s co-founder Sadayuki Furuhashi)......

August 11, 2015

Making Magic with pandas-td

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...

Data Science 101: Interactive Analysis with Jupyter, Pandas and Treasure Data

TD gives you a cloud-based analytics infrastructure accessible via SQL. Our interactive engines like Presto give you the power to crunch billions of records with ease. As a data scientist, you’ll still need to learn how to write basic SQL queries......

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