April 21, 2021The Data & Analytics Maturity Model: What is It and Where Does Your Team Stand?
As customers shift their data infrastructure to cloud data platforms such as Google BigQuery, Snowflake, or Databricks, AtScale is the only provider of a semantic layer and dimensional analysis capability that accelerates performance with no need to extract data from the cloud data warehouse.
Many companies still rely on SSAS for data intelligence and might not realize they can support the same use cases with more open and business-friendly solutions. In this final blog of our 3 part series on leveraging AtScale + Tableau to implement data-driven, self-service BI, we discuss why and how companies are replacing their SSAS frameworks while maintaining continuity for their knowledge workers.
Why data-driven companies are using a Semantic Layer to replace SSAS
Companies are dealing with more data than ever before in the quest for data-driven organizational models and data-centric approaches to business decision-making. That means systems and data teams manage more data sources and storage volume than most legacy systems were designed to handle.
One of the most significant examples is SQL Server Analysis Services (SSAS), a venerable solution set that many companies rely on for dimensional analysis in their data analytics efforts. For SSAS implementations, connecting BI tools to source data means creating or moving a separate copy of that source data. When talking about hundreds or possibly thousands of data sources and petabytes of data, the overhead for these transactions is exceptionally costly. Let’s explore what makes using AtScale’s Semantic Layer so different.
AtScale works directly on any data platform without the need to extract any data or build a physical cube. It connects live data where it resides, delivering information to decision-makers and knowledge workers in real-time without the lag time that can render business intelligence out of date.
With SSAS, you have to move data and build cubes, which carries tremendous overhead and requires highly-skilled data engineering and data science to get right. At American Express, AtScale reduced a four-day cube build to real-time analysis on over 2 billion rows to over 2,000 users. At Home Depot, we facilitated a 14-day cube rebuild containing one quarter’s worth of data to a three-year cube with zero latency.
One of the most pressing challenges for any digital transformation is knowledge workers’ growing pains when learning new tools and processes. AtScale supports leading BI tools such as Tableau and Looker via a JDBC SQL interface and data science tools via a Python interface. SSAS does not. AtScale also supports multi-dimensional models, so you won’t need to port cubes to tabular models. Compatibility with the tools and technologies that data workers use everyday tears down roadblocks to data-driven transformation.
Scalability requirements are difficult to forecast even for experts, especially for SSAS implementations which require a different environment for hosting cubes. AtScale simplifies scalability problems by scaling directly with the data platform and can handle data from multiple data stores. This live connection ensures a near real-time performance experience and a single source of truth no matter how many data sources need support or no matter where they’re located
How EverQuote accelerated data-driven insights by replacing in-house OLAP with AtScale + Tableau
EverQuote is one of the largest online marketplaces for insurance in the world. While the company had a working, data-driven culture, they came up against scalability issues with their home-grown OLAP technologies.
Everquote has built its business on customer empowerment – giving consumers options to protect their most important assets, like family, property, and futures. That’s why the company needed to scale up and continue to deliver data-driven insights to business users across their organization.
The company faced a challenge using legacy technologies to scale self-service analytics to non-technical employees. With AtScale’s Semantic Layer, Everquote seamlessly transitioned its analytics workloads to Snowflake without impacting their business users’ experience. That’s because AtScale enabled business teams to access all of their Snowflake data with the BI tools their users were most comfortable with, such as Tableau and Excel.
By modernizing its data architecture with Snowflake and AtScale, EverQuote delivered democratized data analytics for its business and data science teams. It achieved this while preserving its knowledge workers’ expertise with visualization tools like Tableau, making self-service data analytics across the organization a reality.
Betclic modernized its legacy OLAP with AtScale + Tableau for Cloud-First Data Analytics
Betclic is a European online gambling company that provides millions of players with sports betting, poker, and casino games. These games give the company a massive amount of data, which they leverage to improve their users’ experience and manage the considerable risks associated with the gambling industry. Eventually, the limitations of their legacy SSAS implementation made it challenging to keep pace with growth while maintaining the critical data analytics functions that its business required.
Betclic hosted their SSAS cube-based legacy system on-premises, but they knew it wasn’t scalable long-term. One of their cubes contained two years of customer data and transactions that took over three hours to refresh before the finance teams could leverage it for analytics. With the World Cup approaching, there was an urgent need to modernize their data analytics and transition to a cloud-friendly solution that wouldn’t disrupt their data teams or customer experience.
Betclic used AtScale to underpin their migration to the cloud with Snowflake, helping them seamlessly connect to their preferred BI and visualization tools such as Tableau and Excel.
Once the company implemented AtScale, their business users went from accessing data with poor performance on a legacy cube to a live connection with access to 15 years worth of contextual, relevant information.
AtScale works with leading BI tools like Tableau to help companies replace SSAS in the cloud.
Companies need modernized, cloud-friendly solutions to deliver self-service BI across their organizations and business users. But delivering on the promise of data-driven digital transformation can be a painful process.
AtScale’s Semantic layer greatly simplifies the path of cloud migration, working seamlessly with Snowflake and BI tools like Tableau to help companies scale their analytics and deliver business intelligence to users anywhere.
If you want the full story on how these companies modernized their data analytics and replaced their SSAS capabilities in the cloud using AtScale, check out this webinar or take a look at our case studies.