Introduction to Automated Data Analytics (With Examples)

Is repetitive and menial work impeding your data scientists, analysts, and engineers from delivering their best work? Consider automating your data analytics to free their hands from routine tasks so they can dedicate their time to doing more meaningful, creative work that requires human attention. In this blog we are going to talk about: Now let’s dive in.

Yellowfin Named Embedded Business Intelligence Software Leader in G2 Fall Reports 2022

Yellowfin has again been recognized in the Leader quadrant in the 2022 G2 Fall Grid Reports for Embedded Business Intelligence (Enterprise and Small Business). This is Yellowfin's 13th quarter in a row to be named a leader in a G2 Grid Report. The Yellowfin team are grateful to our customers for the reviews they have provided for our embedded analytics capability and product suite on G2, a leading business software and service comparison source for trusted user ratings and peer-to-peer reviews.

Webinar: Unlocking the Value of Cloud Data and Analytics

From data lakes and data warehouses to data mesh and data fabric architectures, the world of analytics continues to evolve to meet the demand for fast, easy, wide-ranging data insights. Right now, nearly 50% of DBTA subscribers are using public cloud services, and many are investing further in staff, skills, and solutions to address key technical challenges. Even today, the amount of time and resources most organizations spend analyzing data pales in comparison to the effort expended in identifying, cleansing, rationalizing, consolidating, and transforming that data.

Talend's contributions to Apache Beam

Apache Beam is an open-source, unified programming model for batch and streaming data processing pipelines that simplifies large-scale data processing dynamics. The Apache Beam model offers powerful abstractions that insulate you from low-level details of distributed data processing, such as coordinating individual workers, reading from sources and writing to sinks, etc.

Building an automated data pipeline from BigQuery to Earth Engine with Cloud Functions

Over the years, vast amounts of satellite data have been collected and ever more granular data are being collected everyday. Until recently, those data have been an untapped asset in the commercial space. This is largely because the tools required for large scale analysis of this type of data were not readily available and neither was the satellite imagery itself. Thanks to Earth Engine, a planetary-scale platform for Earth science data & analysis, that is no longer the case.

Analyzing satellite images in Google Earth Engine with BigQuery SQL

Google Earth Engine (GEE) is a groundbreaking product that has been available for research and government use for more than a decade. Google Cloud recently launched GEE to General Availability for commercial use. This blog post describes a method to utilize GEE from within BigQuery’s SQL allowing SQL speakers to get access to and value from the vast troves of data available within Earth Engine.

How to simplify and fast-track your data warehouse migrations using BigQuery Migration Service

Migrating data to the cloud can be a daunting task. Especially moving data from warehouses and legacy environments requires a systematic approach. These migrations usually need manual effort and can be error-prone. They are complex and involve several steps such as planning, system setup, query translation, schema analysis, data movement, validation, and performance optimization.