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.
"Never make permanent decisions on temporary feelings". Technology is risky, and architectural decisions can have a considerable effect on the viability of your business. This post lays out strategies to future-proof your data architecture including rules around virtualization, security, reducing complexity.
We’re now witnessing a third wave of innovation in data warehousing technology with the advent of cloud data warehouses. As enterprises move to the cloud, they are abandoning their legacy on-premise data warehousing technologies, including Hadoop, for these new cloud data platforms. This post covers the benefits of a cloud data warehouse and factors to consider when you're ready to make the move.
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.