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  5. Amazon Redshift Spectrum vs Apache Kylin

Amazon Redshift Spectrum vs Apache Kylin

OverviewComparisonAlternatives

Overview

Amazon Redshift Spectrum
Amazon Redshift Spectrum
Stacks99
Followers147
Votes3
Apache Kylin
Apache Kylin
Stacks61
Followers236
Votes24
GitHub Stars3.8K
Forks1.5K

Amazon Redshift Spectrum vs Apache Kylin: What are the differences?

# Introduction

1. **Deployment**: Amazon Redshift Spectrum is a fully managed service offered by AWS that allows you to run queries against exabytes of data in S3 without having to load or transform the data. On the other hand, Apache Kylin needs to be deployed on a Hadoop cluster, which requires more setup and maintenance compared to Redshift Spectrum.
2. **Query Processing Engine**: Redshift Spectrum uses the same query engine as Amazon Redshift, which is based on PostgreSQL, while Apache Kylin uses a distributed computing engine to parallelize queries and process data cubes efficiently.
3. **Data Source**: Amazon Redshift Spectrum is integrated with Amazon S3 for data storage and retrieval, enabling queries to be run directly on data stored in S3. In contrast, Apache Kylin primarily works with data stored in Hadoop-based distributed file systems like HDFS.
4. **Data Model**: Apache Kylin uses a pre-aggregated data model known as data cubes to accelerate query performance for OLAP (Online Analytical Processing) workloads. Redshift Spectrum, on the other hand, does not require pre-aggregated data models and can query data directly from S3.
5. **Cost**: The pricing model for Amazon Redshift Spectrum is based on the amount of data scanned from S3, making it a cost-effective solution for sporadic or unpredictable query workloads. Apache Kylin, being an open-source project, may have lower upfront costs but could require more resources for setup, maintenance, and scaling.
6. **Scalability**: Redshift Spectrum allows you to dynamically scale compute resources based on query demands, which can help handle sudden spikes in workload effectively. However, Apache Kylin's scalability may be limited by the underlying Hadoop cluster's resources and configuration.

In Summary, the key differences between Amazon Redshift Spectrum and Apache Kylin lie in their deployment model, query processing engine, data source integration, data model approach, cost structure, and scalability capabilities.

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Detailed Comparison

Amazon Redshift Spectrum
Amazon Redshift Spectrum
Apache Kylin
Apache Kylin

With Redshift Spectrum, you can extend the analytic power of Amazon Redshift beyond data stored on local disks in your data warehouse to query vast amounts of unstructured data in your Amazon S3 “data lake” -- without having to load or transform any 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.

-
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
99
Stacks
61
Followers
147
Followers
236
Votes
3
Votes
24
Pros & Cons
Pros
  • 1
    Good Performance
  • 1
    Great Documentation
  • 1
    Economical
Pros
  • 7
    Star schema and snowflake schema support
  • 5
    Seamless BI integration
  • 4
    OLAP on Hadoop
  • 3
    Sub-second latency on extreme large dataset
  • 3
    Easy install
Integrations
Amazon S3
Amazon S3
Amazon Redshift
Amazon Redshift
Hadoop
Hadoop
Apache Spark
Apache Spark
Tableau
Tableau
PowerBI
PowerBI
Superset
Superset

What are some alternatives to Amazon Redshift Spectrum, Apache Kylin?

Apache Spark

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.

Presto

Presto

Distributed SQL Query Engine for Big Data

Amazon Athena

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

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

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

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.

Splunk

Splunk

It provides the leading platform for Operational Intelligence. Customers use it to search, monitor, analyze and visualize machine data.

Apache Impala

Apache Impala

Impala is a modern, open source, MPP SQL query engine for Apache Hadoop. Impala is shipped by Cloudera, MapR, and Amazon. With Impala, you can query data, whether stored in HDFS or Apache HBase – including SELECT, JOIN, and aggregate functions – in real time.

Vertica

Vertica

It provides a best-in-class, unified analytics platform that will forever be independent from underlying infrastructure.

Azure Synapse

Azure Synapse

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.

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