Category Archive for: "Integrations"

Supercharge your Salesforce Data Search Efforts with Treasure Data and Elasticsearch

Elasticsearch is growing by the day, and its users include companies like Ebay, Verizon, Reuters, The New York Times, Microsoft, and Facebook (and those are just a few of the bigger ones!) Elasticsearch bene...

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...

Importing and Exporting Google Spreadsheets with Treasure Data

Spreadsheets: Business People's Data Warehouse? Let's admit it. Spreadsheets are not going away. Despite various business intelligence vendors' campaigns against Excel and similar programs, spreadsheets con...

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

Data analytics is a battle between order and chaos. 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 t...

August 18, 2015

5 Tips to Optimize Fluentd Performance

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 computatio...

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

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 interac...

Ruby 101 and Data Collection with Iron.io and Treasure Data

Here at Treasure Data, we aim to give you all the tools you need to need to ramp up with data collection – starting with the basics, using a programming language or environment of your choice – as well as the Treasure Data Service itself, and integration with any number of third-party tools. �...

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