AWS Big Data Blog

How Kyligence Cloud uses Amazon EMR Serverless to simplify OLAP

This post was co-written with Daniel Gu and Yolanda Wang, from Kyligence.

Today, more than ever, organizations realize that modern business runs on data—almost all our interactions with business are based on data, and organizations must use analytics to understand, plan, and improve their operations. That is where Online Analytical Processing (OLAP) comes in. OLAP is designed to manage and analyze big data, enabling organizations to use their data to extract business insights in multiple dimensions.

Kyligence Cloud OLAP solution offers an Intelligent OLAP Platform to simplify multi-dimensional analytics for cloud data lakes. In the past, Kyligence used to deploy and maintain its own Spark clusters based on Amazon Elastic Compute Cloud (Amazon EC2) to handle the multi-dimensional model pre-computing process that required users to build their monitoring and alerting systems to improve the observability and reliability of the Spark cluster. In this post, we present how Kyligence built and end-to-end Kyligence Cloud OLAP solution with Amazon EMR Serverless to simplify deployment and operations, reduce costs, and accelerate time-to-value over the data lake.

What is Amazon EMR Serverless?

Amazon EMR Serverless is a big data cloud platform for running large-scale distributed data processing jobs, and machine learning (ML) applications using open-source analytics frameworks like Apache Spark and Apache Hive. Amazon EMR Serverless makes it easy and cost-effective for data engineers and analysts to run applications without having to tune, operate, optimize, secure, or manage clusters.

What is OLAP?

OLAP is an approach to quickly answer analytics queries at high speeds on large volumes of data, providing capabilities for precomputation, sophisticated data modeling, and multi-dimensional analytics by rolling up large, sometimes separate datasets into a multi-dimensional database known as an OLAP Cube that enables “slicing and dicing” of data from different viewpoints for a streamlined query experience. Apache Kylin, Apache Druid, and ClickHouse are some of the popular OLAP tools.

Although OLAP tools have been successfully used in various industries, they still face many challenges：

  • Dependence on IT organizations – Traditional OLAP tools require complex infrastructure to run large-scale data computing. It requires a large team of IT professionals to operate and maintain this infrastructure, resulting in high costs.
  • Need for large compute resources – Traditional OLAP tools need huge amount of computing resources for processing, and transforming data through a series of specific steps toward a concrete goal. Lack of computational capabilities leads to longer response times, limits the amount of data that can be processed, and impedes the flexibility of the OLAP tool greatly . As a result, data analysts are often confined to narrow datasets, incapable of analyzing all the data freely.
  • Inefficient usage of resources in the cloud – When a large-scale data modeling calculation is performed in the cloud, the cost estimation tools estimate and deploy the corresponding computing resources. However, the utilization rate of resources is often not very high, resulting in inefficient usage of resources.

With OLAP integrated with Amazon EMR Serverless, OLAP tools can use Amazon EMR Serverless as a serverless computing resource pool to complete data processing jobs, which simplifies and enhances user experience.

Kyligence approach to OLAP using Amazon EMR Serverless

Kyligence is an AWS ISV partner that offers an Intelligent OLAP Platform to simplify multi-dimensional analytics for cloud data lakes. As a cloud-native OLAP platform, Kyligence Cloud now integrates with Amazon EMR Serverless to automatically provision Spark to run indexing and building jobs. This empowers you to use all the features and benefits of Kyligence’s OLAP with Amazon EMR Serverless.

Kyligence seamlessly connects to major AWS-native data sources including Amazon Simple Storage Service (Amazon S3), Amazon Redshift, and Amazon Relational Database Service (Amazon RDS) to get the most out of your data on AWS, building a comprehensive AWS big data solution. During data modeling, Kyligence uses Amazon S3 to store the pre-computed data, and serves it for high concurrency queries. Kyligence also seamlessly interfaces with popular business intelligence (BI) tools such as Tableau, Microsoft Power BI, and Microsoft Excel to provide rich, built-in data visualization and self-service tools.

The following diagram illustrates the Kyligence Cloud architecture on AWS.

What you can expect from Kyligence Cloud on AWS

This solution offers the following benefits:

  • High performance – With AWS’s global infrastructure and the distributed computing capabilities of Amazon EMR, Kyligence offers a scalable, cost-effective, high-performance OLAP engine for multi-dimensional analytics. It enables critical data applications and large-scale interactive analytics, and helps you achieve sub-second query response times and high concurrency on PB-scale data.
  • Auto-scaling – Kyligence Cloud’s computing resources can be expanded with one click, and as load decreases, cluster size can be automatically reduced. This auto-scaling capability provides optimized costs with service stability.
  • High compatibility – Kyligence Cloud provides a rich set of APIs (ODBC, JDBC, Rest API, Python Client) and standard ANSI-SQL and XMLA/MDX interface, which can be easily integrated with popular analytics tools like Tableau, Microsoft Excel, Microsoft Power BI, and data science tools like Python.
  • Security and reliability – With Amazon S3, Amazon RDS, Kyligence enterprise-level security features, and AWS Identity and Access Management (IAM) support, Kyligence Cloud safely manages access to the services and resources deployed on AWS while supporting multi-level access control of data models, tables, and cells to ensure data security and privacy protection.
  • One-click deployment on AWS – Kyligence Cloud is available in AWS Marketplace. The deployment is completed automatically based on an AWS CloudFormation template and parameter settings. Kyligence performs automated cluster operation and maintenance, and elastic rule-based cluster scaling, which lightens the workload for IT administrators and cloud infrastructure teams. Kyligence also offers a quick deployment method in the Kyligence Cloud Portal.

How Amazon EMR Serverless integrates with OLAP

With Amazon EMR Serverless, Kyligence Cloud provides out-of-the-box managed Apache Spark services. The Kyligence engine can distribute the compute job to Apache Spark in Amazon EMR Serverless. With the automatic on-demand provisioning and scaling capabilities of Amazon EMR Serverless, Kyligence can quickly meet changing processing requirements at any data volume.

The following diagram illustrates Kyligence Cloud integrated with Amazon EMR Serverless.

Benefits of using Kyligence Cloud with Amazon EMR Serverless

In the past, Kyligence used to deploy and maintain its own Spark clusters based on Amazon Elastic Compute Cloud (Amazon EC2) to handle the multi-dimensional model pre-computing process that required Kyligence users to build their monitoring and alerting systems to improve the observability and reliability of the Spark clusters.

Now, running Kyligence on Amazon EMR Serverless offers a more cost-effective, and high-performance way to run cloud analytics on AWS:

  • Simplified deployment on the cloud – With managed services, you don’t need to consider the lifecycle of the underlying infrastructure and resources. This greatly reduces application complexity and simplifies the deployment of Kyligence Cloud.
  • Improve performance on the cloud – With the help of Amazon EMR Serverless, it provides a refined scaling strategy, which can help Kyligence Cloud spin up and recycle resources faster. In Kyligence performance benchmark testing, we observed 15–20% faster performance compared to open-source Spark cluster for index building.
  • Reduce the difficulty of operation and maintenance – With the help of Amazon EMR Serverless capabilities, operation and maintenance personnel can easily maintain the capacity and running status of computing resources without having to understand the underlying analysis framework.
  • Cost-optimization on the cloud – Amazon EMR Serverless provides a refined scaling strategy that can automatically determine the resources that the application needs, acquires these resources to process your jobs, and releases the resources when the jobs complete. You only pay for the resources used by the application, which helps reduce the Total Cost of Operations (TCO) on the cloud.

Get started with Kyligence Cloud on Amazon EMR Serverless

You can get started with the full potential of Kyligence Cloud on the AWS Marketplace or quickly test drive Kyligence.

To use Amazon EMR Serverless, you just need to select Serverless Spark on the Build Cluster tab during deployment.

Conclusion

Using managed and scalable services like Amazon EMR Serverless allows Kyligence users to speed up self-service analytics on large volumes of data, and maintain a relatively simplified architecture. With this solution, you can now concentrate on business demands instead of technical issues.

About Kyligence

Kyligence was founded in 2016 by the original creators of Apache Kylin™, the leading open-source OLAP for big data. Kyligence offers an Intelligent OLAP Platform to simplify multi-dimensional analytics for cloud data lakes.

For more information, visit Kyligence.

About the authors

Daniel Gu is a senior product manager on the Kyligence Cloud Team, who manages products and services and conducts research to determine the viability of products in the cloud.

Yolanda Wang is a senior product marketing manager at Kyligence, who owns the positioning, messaging, and branding of Kyligence products and works with various teams to drive go-to-market strategies.

Kiran Guduguntla is a WW Go-to-Market Specialist for Amazon EMR at AWS. He works with AWS customers across the globe to strategize, build, develop, and deploy modern data analytics solutions.