Competition for consumer spend is a make-or-break proposition. Will retailers know their customer? Will they act with the speed and scale necessary to compete? If they take steps to fully leverage the cloud, they will.
Data transformation is the T in ETL - it's one-third of the holy trinity of Extract, Translate & Load (ETL). In the ETL process, Transform is the process of converting the extracted data from its previous form into the form it needs to be in so that it can be placed into another database.
The concept of virtualization is powerful and nuanced. There are many ways to virtualize data, and AtScale employs several of these methods to make deploying data services faster, more performant, secure and correct. One such interpretation of virtualization is representing diverse data from different origins as one “unified” database.
Business Intelligence (BI) transforms how enterprises make decisions by delivering insights across the entire business. These insights are limited only by the curiosity of the people asking the questions and the speed at which the systems and software that support analytics can perform. BI relies on acceleration and performance (a.k.a. “speed”) to enable data exploration and data mining at a tempo that keeps the human brain engaged.
Salesforce acquires Tableau for $16B, adding momentum to the rapidly growing analytics space and further highlighting the need for AtScale's unique analytics architecture.
AtScale CEO Chris Lynch provides his perspective on the exciting acquisition of Looker by Google.
AtScale’s newest release 2019.1 helps us stay true to our mission of helping enterprises realize the value of their legacy platform investments and capitalize on the speed at which data is proliferating, specifically addressing the challenges associated with migrating from on-prem to cloud, RDBMS to Hadoop, and legacy BI to advanced analytics. Read on for more information about this product release.
On May 25th, 2018 the EU enacted the new General Data Protection Regulations (GDPR). Now, one year on, the tentative returns are in. If you’re in the business of cloud transformation and responsible data deployment, you need to put a spotlight on data integrity and governance.
Data virtualization connects data silos and enables a single view of data without having to physically integrate it. Learn more about data virtualization in the enterprise.
The CDO presents an opportunity to define the priorities and pain points for a company’s most important asset: Data. Understand the 7 keys to this role.
As the BI market continues to mature, Tableau and Microsoft retain their positions as the dominant players in Gartner's 2019 magic quadrant on BI and Analytics. ThoughtSpot and Looker are the two emerging vendors to watch.
As part of its new partnership with Snowflake, AtScale has implemented features that augment Snowflake's use of different-sized warehouses to optimize cost and compute.
Rakuten, a leading cashback and rewards website, initially used AtScale to improve analytics performance on Hadoop. AtScale enabled Rakuten to seamlessly migrate to Snowflake while continuing to optimize performance on their new cloud data platform.
Enterprises need a Virtual Data Warehouse to provide a single view of their data regardless of where it is stored, accelerate time to insight, and keep data secure.
AtScale empowers enterprises to benefit from Google BigQuery while keeping analysts on Microsoft Excel.
Learn how AtScale enables enterprises to optimize cloud costs and increase query performance on any cloud environment.
Enterprises are trusting cloud data warehouses with greater frequency. Amazon Redshift, Google, and Snowflake are three of the main players in the CDW space.
OLAP emerged in the 1990s with the releases of tools like Essbase and Microsft SQL Server Analysis Services. However, OLAP has been plagued by inefficiency as data volumes have exploded. AtScale's Virtual Data Warehouse empowers enterprises to realize strong BI performance with OLAP.
Hadoop was created with the goal of lowering the cost of analyzing data, but implementations often resulted in similar BI performance challenges. AtScale's use of in-memory aggregates alleviates many of the inefficiencies of BI on Hadoop.
Data architects often have to go to great lengths to model complex business logic in a scalable fashion. AtScale enables this modeling to take place away from BI consumers, allowing more efficient analysis of complex business information.