Apache Kylin vs Azure Synapse

Overview

Apache Kylin

Stacks61

Followers236

Votes24

GitHub Stars3.8K

Forks1.5K

Azure Synapse

Stacks104

Followers230

Votes10

Apache Kylin vs Azure Synapse: What are the differences?

Introduction

Apache Kylin and Azure Synapse are both big data platforms that provide analytics and processing capabilities for large datasets.

Architecture: Apache Kylin is an OLAP (Online Analytical Processing) engine that uses pre-aggregated data cubes for fast query processing, while Azure Synapse is an integrated analytics service that combines data integration, data warehousing, and big data analytics in one platform.
Compatibility: Apache Kylin is specifically designed to work with Apache Hadoop and Apache Spark, while Azure Synapse can integrate with a wide variety of data sources including Azure Blob Storage, SQL Data Warehouse, and Cosmos DB.
Scalability: Apache Kylin can handle large amounts of data but may require additional configurations for high scalability, while Azure Synapse is built with built-in scalability features to handle large datasets and high query concurrency without much manual tuning.
Cost: Apache Kylin is an open-source project with no licensing costs, but the deployment and maintenance may require skilled resources, whereas Azure Synapse is a cloud-based service with a pay-as-you-go pricing model that includes the cost of storage, compute, and other resources.
Advanced Analytics: Azure Synapse offers a wide range of advanced analytics capabilities such as machine learning, real-time analytics, and AI integration, while Apache Kylin focuses primarily on OLAP and cube-based analytics capabilities.
Ecosystem Integration: Apache Kylin has deep integration with the Apache ecosystem and works well with various open-source tools, while Azure Synapse integrates seamlessly with other Microsoft services such as Power BI, Azure Data Factory, and Azure Machine Learning.

In Summary, Apache Kylin and Azure Synapse differ in terms of architecture, compatibility, scalability, cost, advanced analytics capabilities, and ecosystem integration.

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Detailed Comparison

Apache Kylin	Azure Synapse
Apache Kylin™ is an open source Distributed Analytics Engine designed to provide SQL interface and multi-dimensional analysis (OLAP) on Hadoop/Spark supporting extremely large datasets, originally contributed from eBay Inc.	It is an analytics service that brings together enterprise data warehousing and Big Data analytics. It gives you the freedom to query data on your terms, using either serverless on-demand or provisioned resources—at scale. It brings these two worlds together with a unified experience to ingest, prepare, manage, and serve data for immediate BI and machine learning needs.
Extremely Fast OLAP Engine at Scale; ANSI SQL Interface on Hadoop; Interactive Query Capability; MOLAP Cube; Seamless Integration with BI Tools	Complete T-SQL based analytics – Generally Available; Deeply integrated Apache Spark; Hybrid data integration; Unified user experience
Statistics
GitHub Stars 3.8K	GitHub Stars -
GitHub Forks 1.5K	GitHub Forks -
Stacks 61	Stacks 104
Followers 236	Followers 230
Votes 24	Votes 10
Pros & Cons
Pros 7 Star schema and snowflake schema support 5 Seamless BI integration 4 OLAP on Hadoop 3 Easy install 3 Sub-second latency on extreme large dataset	Pros 4 ETL 3 Security 2 Serverless 1 Doesn't support cross database query Cons 1 Dictionary Size Limitation - CCI 1 Concurrency
Integrations
Hadoop Apache Spark Tableau PowerBI Superset	No integrations available

What are some alternatives to Apache Kylin, Azure Synapse?

Metabase

It is an easy way to generate charts and dashboards, ask simple ad hoc queries without using SQL, and see detailed information about rows in your Database. You can set it up in under 5 minutes, and then give yourself and others a place to ask simple questions and understand the data your application is generating.

Google BigQuery

Run super-fast, SQL-like queries against terabytes of data in seconds, using the processing power of Google's infrastructure. Load data with ease. Bulk load your data using Google Cloud Storage or stream it in. Easy access. Access BigQuery by using a browser tool, a command-line tool, or by making calls to the BigQuery REST API with client libraries such as Java, PHP or Python.

Apache Spark

Spark is a fast and general processing engine compatible with Hadoop data. It can run in Hadoop clusters through YARN or Spark's standalone mode, and it can process data in HDFS, HBase, Cassandra, Hive, and any Hadoop InputFormat. It is designed to perform both batch processing (similar to MapReduce) and new workloads like streaming, interactive queries, and machine learning.

Amazon Redshift

It is optimized for data sets ranging from a few hundred gigabytes to a petabyte or more and costs less than $1,000 per terabyte per year, a tenth the cost of most traditional data warehousing solutions.

Qubole

Qubole is a cloud based service that makes big data easy for analysts and data engineers.

Presto

Distributed SQL Query Engine for Big Data

Amazon EMR

It is used in a variety of applications, including log analysis, data warehousing, machine learning, financial analysis, scientific simulation, and bioinformatics.

Amazon Athena

Amazon Athena is an interactive query service that makes it easy to analyze data in Amazon S3 using standard SQL. Athena is serverless, so there is no infrastructure to manage, and you pay only for the queries that you run.

Superset

Superset's main goal is to make it easy to slice, dice and visualize data. It empowers users to perform analytics at the speed of thought.

Apache Flink

Apache Flink is an open source system for fast and versatile data analytics in clusters. Flink supports batch and streaming analytics, in one system. Analytical programs can be written in concise and elegant APIs in Java and Scala.

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Apache Kylin vs Azure Synapse: What are the differences?

Introduction

Apache Kylin and Azure Synapse are both big data platforms that provide analytics and processing capabilities for large datasets.

Architecture: Apache Kylin is an OLAP (Online Analytical Processing) engine that uses pre-aggregated data cubes for fast query processing, while Azure Synapse is an integrated analytics service that combines data integration, data warehousing, and big data analytics in one platform.
Compatibility: Apache Kylin is specifically designed to work with Apache Hadoop and Apache Spark, while Azure Synapse can integrate with a wide variety of data sources including Azure Blob Storage, SQL Data Warehouse, and Cosmos DB.
Scalability: Apache Kylin can handle large amounts of data but may require additional configurations for high scalability, while Azure Synapse is built with built-in scalability features to handle large datasets and high query concurrency without much manual tuning.
Cost: Apache Kylin is an open-source project with no licensing costs, but the deployment and maintenance may require skilled resources, whereas Azure Synapse is a cloud-based service with a pay-as-you-go pricing model that includes the cost of storage, compute, and other resources.
Advanced Analytics: Azure Synapse offers a wide range of advanced analytics capabilities such as machine learning, real-time analytics, and AI integration, while Apache Kylin focuses primarily on OLAP and cube-based analytics capabilities.
Ecosystem Integration: Apache Kylin has deep integration with the Apache ecosystem and works well with various open-source tools, while Azure Synapse integrates seamlessly with other Microsoft services such as Power BI, Azure Data Factory, and Azure Machine Learning.

In Summary, Apache Kylin and Azure Synapse differ in terms of architecture, compatibility, scalability, cost, advanced analytics capabilities, and ecosystem integration.