Apache Kylin vs Azure HDInsight

Overview

Azure HDInsight

Stacks29

Followers138

Votes0

Apache Kylin

Stacks61

Followers236

Votes24

GitHub Stars3.8K

Forks1.5K

Apache Kylin vs Azure HDInsight: What are the differences?

Apache Kylin and Azure HDInsight are two widely used big data processing platforms. Below are key differences between Apache Kylin and Azure HDInsight.

Processing Engines: Apache Kylin leverages Apache Hadoop and Apache Spark for distributed computing, while Azure HDInsight supports a range of processing engines including Apache Hadoop, Apache Spark, Apache Hive, and Apache HBase. This gives Azure HDInsight users more flexibility in choosing the right engine for their specific requirements.
Deployment Model: Apache Kylin is typically deployed on-premises or on cloud infrastructure provided by third-party vendors, whereas Azure HDInsight is a cloud-based offering from Microsoft that provides users with managed clusters for big data processing. This difference in deployment models can impact factors such as scalability, maintenance, and integration with other cloud services.
Resource Management: Apache Kylin has its own resource scheduling and management mechanisms, while Azure HDInsight integrates with Azure's resource management capabilities such as Azure Resource Manager for provisioning and managing resources. This integration can simplify resource management tasks for Azure HDInsight users and provide better integration with other Azure services.
Security Features: Apache Kylin has its own set of security features and mechanisms for securing data and access to resources, while Azure HDInsight integrates with Azure's built-in security features such as Azure Active Directory for authentication and authorization. This integration with Azure's security services can provide tighter control over data access and security policies.
Pricing Model: Apache Kylin is an open-source project with no licensing costs, but users are responsible for managing and maintaining their own infrastructure. On the other hand, Azure HDInsight is a paid service with pricing based on factors such as cluster size, storage, and processing engines used. This difference in pricing models can impact the total cost of ownership and scalability of the big data processing platform.

In Summary, Apache Kylin and Azure HDInsight differ in terms of processing engines, deployment models, resource management, security features, and pricing models.

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Detailed Comparison

Azure HDInsight	Apache Kylin
It is a cloud-based service from Microsoft for big data analytics that helps organizations process large amounts of streaming or historical data.	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.
Fully managed; Full-spectrum; Open-source analytics service in the cloud for enterprises	Extremely Fast OLAP Engine at Scale; ANSI SQL Interface on Hadoop; Interactive Query Capability; MOLAP Cube; Seamless Integration with BI Tools
Statistics
GitHub Stars -	GitHub Stars 3.8K
GitHub Forks -	GitHub Forks 1.5K
Stacks 29	Stacks 61
Followers 138	Followers 236
Votes 0	Votes 24
Pros & Cons
No community feedback yet	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
Integrations
IntelliJ IDEA Apache Spark Kafka Visual Studio Code Hadoop Apache Storm HBase Apache Hive Azure Data Factory Azure Active Directory	Hadoop Apache Spark Tableau PowerBI Superset

What are some alternatives to Azure HDInsight, Apache Kylin?

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.

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.

lakeFS

It is an open-source data version control system for data lakes. It provides a “Git for data” platform enabling you to implement best practices from software engineering on your data lake, including branching and merging, CI/CD, and production-like dev/test environments.

Druid

Druid is a distributed, column-oriented, real-time analytics data store that is commonly used to power exploratory dashboards in multi-tenant environments. Druid excels as a data warehousing solution for fast aggregate queries on petabyte sized data sets. Druid supports a variety of flexible filters, exact calculations, approximate algorithms, and other useful calculations.

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Apache Kylin vs Azure HDInsight: What are the differences?

Apache Kylin and Azure HDInsight are two widely used big data processing platforms. Below are key differences between Apache Kylin and Azure HDInsight.

Processing Engines: Apache Kylin leverages Apache Hadoop and Apache Spark for distributed computing, while Azure HDInsight supports a range of processing engines including Apache Hadoop, Apache Spark, Apache Hive, and Apache HBase. This gives Azure HDInsight users more flexibility in choosing the right engine for their specific requirements.
Deployment Model: Apache Kylin is typically deployed on-premises or on cloud infrastructure provided by third-party vendors, whereas Azure HDInsight is a cloud-based offering from Microsoft that provides users with managed clusters for big data processing. This difference in deployment models can impact factors such as scalability, maintenance, and integration with other cloud services.
Resource Management: Apache Kylin has its own resource scheduling and management mechanisms, while Azure HDInsight integrates with Azure's resource management capabilities such as Azure Resource Manager for provisioning and managing resources. This integration can simplify resource management tasks for Azure HDInsight users and provide better integration with other Azure services.
Security Features: Apache Kylin has its own set of security features and mechanisms for securing data and access to resources, while Azure HDInsight integrates with Azure's built-in security features such as Azure Active Directory for authentication and authorization. This integration with Azure's security services can provide tighter control over data access and security policies.
Pricing Model: Apache Kylin is an open-source project with no licensing costs, but users are responsible for managing and maintaining their own infrastructure. On the other hand, Azure HDInsight is a paid service with pricing based on factors such as cluster size, storage, and processing engines used. This difference in pricing models can impact the total cost of ownership and scalability of the big data processing platform.

In Summary, Apache Kylin and Azure HDInsight differ in terms of processing engines, deployment models, resource management, security features, and pricing models.