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Data Enrichment in the Cloud – Why Data Marketplaces need Data Prep

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Leverage Cloud Functions and APIs to Monitor Cloud Dataprep Jobs Status in a Google Sheet

If you manage a data and analytics pipeline in Google Cloud, you may want to monitor it and obtain a... more

  |  July 28, 2020

Automate your Google Cloud Data Warehouse on BigQuery with Cloud Dataprep

Today’s blog is a guest post from Geoffroy de Viaris, a Data and Analytics Project Manager at a high... more

  |  June 11, 2020

How to Automate a Cloud Dataprep Pipeline When a File Arrives

With a better mastery of Cloud Functions, you can trigger a Dataprep job via API when a file lands in a Cloud... more

  |  May 26, 2020

What is Data Structure? Using Basic Data Structures to Organize Like Martha Stewart

How can Martha Stewart be of any relevance in a blog post titled “What is data structure?” Stay with us... more

  |  May 19, 2020

Building An Effective Cloud Data Lake with Trifacta

Ask a number of organizations what kind of data they use and they’ll likely each give you a different... more

  |  March 24, 2020
 

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ETL Tools and Data Wrangling: What’s the Difference?

The extract, transform, and load process (ETL process), and ETL tools, have been the de facto way to move and... more

  |  March 17, 2020

Why Cloud-Native Data Preparation is Superior to Desktop Alternatives

Happy 2020 everyone! A new decade comes with new perspectives. For companies striving to use data to drive... more

  |  February 4, 2020

Is ETL Dead? ETL vs. Data Wrangling in the Cloud

Is ETL dead? It’s a question that has come up a lot in recent years as organizations modernize their... more

  |  January 21, 2020

Five Tips for Optimizing Self-Service Analytics on Google Cloud Platform: Part Five

So you’ve decided to transition (at least in part) your data analytics to the cloud. More specifically, you... more

  |  January 6, 2020

What is Azure Data Lake and How to Refine It with a Modern Data Wrangling Solution

Microsoft Azure Data Lake is a highly scalable cloud service that allows developers, scientists, business... more

  |  December 31, 2019

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Five Tips for Optimizing Self-Service Analytics on Google Cloud Platform: Part Four

So you’ve decided to transition (at least in part) your data analytics to the cloud. More specifically, you... more

  |  December 18, 2019

What’s New For Trifacta On Microsoft Azure

As the global leader in data wrangling, Trifacta continues to innovate by introducing new capabilities on... more

  |  December 16, 2019

Five Tips for Optimizing Self-Service Analytics on Google Cloud Platform: Part Two

So you’ve decided to transition (at least in part) your data analytics to the cloud. More specifically,... more

  |  November 21, 2019

Five Tips for Optimizing Self-Service Analytics on Google Cloud Platform: Part One

So you’ve decided to transition (at least in part) your data analytics to the cloud. More specifically,... more

  |  November 12, 2019

Snowflake Data Prep for Data Scientists, Data Analysts and Data Engineers

Snowflake’s unique architecture allows organizations to store a wider variety of data formats and data... more

  |  October 29, 2019

Google Data Fusion and Other Transformation Services for BigQuery Dataprep

The good news is, when it comes to moving and transforming data for analytics built on top of BigQuery, the... more

  |  October 25, 2019

Cleaning Data From Snowflake Database Storage Layers

What is a Snowflake database? Snowflake is a cloud data warehouse that provides various layers for cloud... more

  |  October 24, 2019

Data Preparation Best Practices for Snowflake’s Cloud Data Warehouse

Snowflake is known for their separation of storage and compute, which makes scaling data more efficient.... more

  |  October 1, 2019