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Microsoft Azure has seen explosive growth in the last couple
years. In business intelligence, analytics, and data science areas, Azure
provides a rich set of services that enable data scientists and analysts to
work on large and complex data sets to deliver business value.
Microsoft partners from around the world gathered at last month’s Microsoft Inspire conference to get a first look at Microsoft’s latest technology. This year, Azure migration was a major focus. Migrating to Azure not only helps control operations and infrastructure costs, it also opens the door to additional analytics use cases.
Azure SQL Data Warehouse is Microsoft’s cloud data warehouse
offering. It seamlessly integrates with Azure Active Directory, Azure Data Factory,
Azure Data Lake Storage, Azure Databricks, and Microsoft Power BI. It also
works well with other integration and business intelligence tools on the
market. Some key features of Azure SQL Data Warehouse include elastic scaling,
unlimited storage, automated administration, and advanced workload management.
The following is a diagram of an Azure Modern Data Warehouse solution:
SQL Server Analytic Service (SSAS) is a query acceleration layer that sits on top of data warehouses. It reads data from star or snowflake schemas in the data warehouses and calculates the aggregations. The results of these calculations are stored in a data structure called a cube.
This type of analysis is referred to as multidimensional analysis, and the related software is called MOLAP (Multidimensional OnLine Analytical Processing).
With multidimensional analytics, aggregation queries are
answered by simple lookups into the cubes. This ensures fast, predictable, and
guaranteed query response times. Since SSAS isn’t built on a distributed
architecture, server configuration limits the total data and concurrent users
it can support. In other words, you’d have to scale up to process larger
Unfortunately, SSAS is not available in the cloud. The closest service to SSAS in diagram 1 is Azure Analytics Service. Azure Analytics Service, while providing a unified semantic layer to the BI tools, does not have multidimensional analytics capabilities. Once migrated to the cloud, customers who have been running enterprise data warehouses and cubes on premises will find out that they are not able to click their dashboards interactively (as they are used to doing on premises).
Fortunately, Kyligence can fill in this gap in the Azure Analytics environment. Kyligence Cloud Big Data analytics platform offers a managed augmented OLAP analytics service in the cloud. It leverages cloud-native computing and storage infrastructure. This enables fast, elastic and cost-effective analytics innovation, with any data lake and at any scale.
On the Azure platform, Kyligence reads data from Azure SQL
Data Warehouse and generates pre-calculated aggregations. It then stores the
result in Azure Data Lake Storage. It exposes ANSI SQL interfaces to Power BI
or other BI tools.
Kyligence Cloud also provides cluster deployment and
management, account management, and online diagnosis capabilities. It can also
serve as the semantic layer for the BI tools.
Built on a distributed architecture, Kyligence Cloud can easily scale out to support a large amount of data and concurrent users. Recently, during a test for a major financial services company, we built aggregations for 350 billion rows of data on Azure. Enterprises use Kyligence’s OLAP engine to build cubes for billions of records to serve 1000s of concurrent users.
Kyligence goes beyond simply addressing the limitations of a SSAS-based approach. Enterprises adopting Kyligence as a solution for Big Data analytics on the cloud can also realize these additional benefits:null
Once the cube is created, users can slice and dice data any way they want. It is very easy to support new requirements that need different queries and aggregations. End users can create new charts in the dashboard and see the results instantaneously.
Kyligence’s intelligent modeling and optimization
capabilities automatically analyze query performance and cube consumption
statistics in the background. This allows it to make necessary adjustments
without requiring user intervention.
Because aggregation is pre-calculated, there many fewer run
time aggregations happening in the query. This reduces compute cost in the
cloud. Even with increased storage costs (to store the pre-calculations),
enterprises still see dramatic cloud cost savings.
In the architecture diagram above (Diagram 4), Azure SQL
Data Warehouse stores historical transactions and Kyligence stores aggregated
results. Kyligence can also serve as the unified query entry point for both
aggregated and detailed queries.
If a BI tool issues an aggregated query, Kyligence will simply look up the cube and get the result. If the BI tool asks for a specific transaction, Kyligence can route the query to the data warehouse. Azure SQL Data Warehouse will execute the query, fetch the transaction, and sends the result back to the BI tool.
If you're curious about what Kyligence Cloud looks like in action, this demo provides a helpful overview:
There has never been a better time to start migrating your
analytics to the cloud. Technological advancements in cloud computing platforms
(like Azure) have made transitioning a breeze. While the path forward may
appear more streamlined, selecting the right cloud software and partners to
help support that migration is key for success in the cloud.
If you’re ready to take your next steps towards migration, or wish to improve BI performance on the cloud platform you’re already using, check out our cloud big data platform and other extreme OLAP BI solutions.
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