The decision to transform pre-load (ETL) or post-load (ELT) can be heavily influenced by many factors: the complexity of transformation, nature of load technology, and volume of data. This post outlines the differences between ETL and ELT, the impact of each on performance, and how to choose.
This post outlines five key things to keep in mind as you consider making the complex but worth-it migration to the cloud - to avoid the "gotchas".
Enterprises migrating to the cloud must go beyond simply “lifting and shifting” data into the cloud and embrace an approach that fundamentally transforms how data will be used. By abstracting data infrastructure, unifying security and data governance and providing flexible and consistent access to data, enterprises will be able to leverage superior insights and coordination that will directly support the business’s bottom line.
A data virtualization solution that helps enterprises de-emphasize technology so that business users can have simple, intuitive access to as much accurate data as possible, is the best solution for far-seeing companies.
The size and complexity of enterprise data ecosystems means that cloud transformation presents many challenges. Intelligent data virtualization is the missing bridge that serves not just technical needs, but larger business objectives, and that will unlock an enterprise's shared data intellect.
Enterprises tackling the challenge of data governance across the hybrid cloud apply intelligent data virtualization to create a central location for data and model discovery with automated data governance.
If your company isn’t good at analytics, it’s not ready for AI. Companies who rush into artificial intelligence before mastering the fundamentals of data engineering and analytics will end up paralyzed. This post from AtScale Co-founder and CTO Matt Baird emphasizes the need to get the right data, focus, and understand how to use it.
Virtualization uses the concepts of abstraction to decouple data-consuming clients from the means of materializing the answers to their questions. As users demand more sophisticated software, and business environments evolve, inevitably the complexity of their data environments increases dramatically. Unchecked, the conflagration resulting in these two natural vectors is the perfect storm for massive security breaches.
Data virtualization gives retailers a competitive edge in five key areas of their operations, as well as the benefits of a shared data intellect today, no matter where they are in their cloud migration journey.
DataOps will help organizations clear technical hurdles in order to develop cross-functional programs that use data more effectively.
New data model advancements (nested and non-scalar data types) for data lakes and cloud data warehouses are a game changer. However, the existing BI and AI toolsets are really not geared to take advantage of these new innovations. AtScale’s Adaptive Cachet will readily accept your existing star schemas and optimize them for the data denormalization and full table scans these data platforms prefer automatically.
Organizations migrating to the cloud are missing a crucial element that is slowing and complicating the migration process. Short circuit the challenges of cloud migration with a new perspective on data access and management.
AtScale and Google BigQuery together provide a cost effective, business friendly, analytics solution that includes a universal semantic layer, predictable performance, data governance and security all in one.
Four Strategies for Risk-Free Cloud Data Migration: This industry guide provides four practical ways intelligent data virtualization can improve the cloud data migration journey.
AtScale’s 2019.2 product release introduces a time-series and time-relative analysis capability for large volumes of data across disparate databases and platforms. This enables data analyst and data science teams to easily access large volumes of time-series data and quickly query and configure data for any (BI), artificial intelligence (AI) or machine learning (ML) tool.
This blog reveals the seven key challenges an organization must overcome in order for their cloud data transformation process to succeed, and explains how Virtual Data Warehouses are the best solution to doing so.
What businesses need is a solution that intelligently virtualizes all their siloed data into a single, unified data view from which a variety of BI tools can get fast, consistent answers. AtScale's Virtual Data Warehousing solves the critical challenges facing companies with large data warehouses looking to move to the cloud.
Combine Snowflake with AtScale and you get an analytics platform that can handle the biggest data with the most complex analytics at scale.
Data extraction is perhaps the most important part of the Extract/Translate/Load (ETL) process because it inherently includes the decision making on which data is most valuable for achieving the business goal driving the overall ETL. These decisions will heavily influence the viability of downstream use of the data, so it's critical to ensure there's a correlation between the data you are extracting and the decisions it will be used for.
Data streaming is important when real-time analysis is required, for retail customer experience, cyber security monitoring, and weather safety. This post explains data streaming vs. batch data movement, and the challenges and technologies associated with streaming.