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Apache Kylin VS Amazon Redshift

Compare Apache Kylin VS Amazon Redshift and see what are their differences

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Apache Kylin logo Apache Kylin

OLAP Engine for Big Data

Amazon Redshift logo Amazon Redshift

Learn about Amazon Redshift cloud data warehouse.
  • Apache Kylin Landing page
    Landing page //
    2023-06-29
  • Amazon Redshift Landing page
    Landing page //
    2023-03-14

Apache Kylin features and specs

  • High Query Performance
    Apache Kylin is designed for high-performance, low-latency analytics on large datasets. Its OLAP engine pre-computes and stores aggregated queries, which speeds up query responses significantly.
  • Scalability
    Kylin can handle massive volumes of data, making it suitable for large scale data warehousing needs. It is designed to scale out by distributing the workload across a cluster of servers.
  • Integration with Hadoop Ecosystem
    Kylin integrates seamlessly with the Hadoop ecosystem, leveraging tools like Hive, HBase, and Spark to facilitate data processing and storage, thereby enhancing its functionality and compatibility.
  • Support for Multi-dimensional Analysis
    It provides strong multidimensional analysis capabilities, allowing for complex queries using well-known BI tools like Tableau and Power BI.

Possible disadvantages of Apache Kylin

  • Complex Setup
    Setting up and configuring Apache Kylin can be complex and time-consuming, requiring a deep understanding of the Hadoop ecosystem and its components.
  • Resource Intensity
    The pre-computation of data cubes and their storage can be resource-intensive, consuming significant memory and storage capacity.
  • Limited Flexibility in Querying
    Pre-aggregated cube-based analysis may not cover all ad-hoc queries. Kylin's strength lies in pre-aggregated queries but may fall short in handling highly dynamic, on-the-fly queries.
  • Maintenance Overhead
    Maintaining Kylin’s precomputed cubes can become cumbersome, particularly as data evolves or changes frequently, requiring updates or recalculations of cubes.

Amazon Redshift features and specs

  • Scalability
    Amazon Redshift allows you to scale your data warehouse up or down easily based on your needs with just a few clicks or by using the API, providing flexibility to handle varying workloads.
  • Performance
    Redshift uses columnar storage, parallel processing, and efficient data compression techniques to deliver high performance for complex queries and large datasets.
  • Integration
    It seamlessly integrates with various AWS services like S3, DynamoDB, and QuickSight, making it easier to build a comprehensive data ecosystem.
  • Cost-effective
    Redshift offers a pay-as-you-go pricing model with no upfront costs, and you can save more with reserved instances, making it cost-effective for many businesses.
  • Security
    It includes features like encryption, Virtual Private Cloud (VPC), and compliance certifications (such as SOC 1, SOC 2, SOC 3, and more) to ensure data security and compliance.
  • Managed Service
    Amazon Redshift is a fully managed service, so it takes care of managing, monitoring, and scaling the infrastructure, allowing you to focus on your data and insights.

Possible disadvantages of Amazon Redshift

  • Complexity
    Although Redshift is powerful, it can be complex to set up, configure, and optimize for best performance, requiring knowledge and experience in data warehousing.
  • Cost for Unused Resources
    While Redshift is cost-effective for large-scale operations, costs can add up quickly if resources are not managed properly, especially with long-running clusters that are under-utilized.
  • Maintenance Windows
    Despite being a managed service, maintenance windows and updates can occasionally lead to downtime or performance degradation, impacting availability.
  • Data Transfer Costs
    Transferring data in and out of Redshift can incur additional costs, particularly if large volumes of data are involved, which can affect overall budget planning.
  • Vendor Lock-in
    Using Amazon Redshift ties you to the AWS ecosystem, which could be a disadvantage if you are considering a multi-cloud strategy or planning to switch providers in the future.

Apache Kylin videos

Extreme OLAP Analytics with Apache Kylin - Big Data Application Meetup

More videos:

  • Review - Apache Kylin: OLAP Cubes for NoSQL Data stores
  • Review - Installing Apache Kylin in Cloudera Quickstart VM Sandbox

Amazon Redshift videos

Getting Started with Amazon Redshift - AWS Online Tech Talks

More videos:

  • Review - Amazon Redshift Materialized Views
  • Tutorial - Amazon Redshift Tutorial | Amazon Redshift Architecture | AWS Tutorial For Beginners | Simplilearn

Category Popularity

0-100% (relative to Apache Kylin and Amazon Redshift)
Databases
20 20%
80% 80
Big Data
22 22%
78% 78
Relational Databases
100 100%
0% 0
Data Management
0 0%
100% 100

User comments

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Reviews

These are some of the external sources and on-site user reviews we've used to compare Apache Kylin and Amazon Redshift

Apache Kylin Reviews

We have no reviews of Apache Kylin yet.
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Amazon Redshift Reviews

Data Warehouse Tools
No, SQL (Structured Query Language) is not a data warehouse itself. SQL is a programming language used for managing and querying data stored in relational database management systems (RDBMS) and data warehouses. Many data warehouse solutions, such as Peliqan, Amazon Redshift, and PostgreSQL, support SQL for querying and analyzing data within the data warehouse
Source: peliqan.io
Top 6 Cloud Data Warehouses in 2023
Coined in November 2021, Amazon Redshift was launched as a fully managed cloud data warehouse that can handle petabyte-scale data. While it was not the first cloud data warehouse, it became the first to proliferate in the market share after a large-scale adoption. Redshift uses SQL dialect based on PostgreSQL, which is well-known by many analysts globally, and its...
Source: geekflare.com
Top 5 Cloud Data Warehouses in 2023
Jan 11, 2023 The 5 best cloud data warehouse solutions in 2023Google BigQuerySource: https://cloud.google.com/bigqueryBest for:Top features:Pros:Cons:Pricing:SnowflakeBest for:Top features:Pros:Cons:Pricing:Amazon RedshiftSource: https://aws.amazon.com/redshift/Best for:Top features:Pros:Cons:Pricing:FireboltSource: https://www.firebolt.io/Best for:Top...
Top 5 BigQuery Alternatives: A Challenge of Complexity
As the most proven tool in this category, Amazon Redshift is a fully managed cloud-based data warehouse used to collect and store data. Like BigQuery, Redshift seamlessly integrates with multiple products and ETL services.
Source: blog.panoply.io

Social recommendations and mentions

Based on our record, Amazon Redshift seems to be a lot more popular than Apache Kylin. While we know about 29 links to Amazon Redshift, we've tracked only 1 mention of Apache Kylin. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.

Apache Kylin mentions (1)

  • Apache Kafka Use Cases: When To Use It & When Not To
    A Kafka-based data integration platform will be a good fit here. The services can add events to different topics in a broker whenever there is a data update. Kafka consumers corresponding to each of the services can monitor these topics and make updates to the data in real-time. It is also possible to create a unified data store through the same integration platform. Developers can implement a unified store either... - Source: dev.to / over 2 years ago

Amazon Redshift mentions (29)

  • How to Pitch Your Boss to Adopt Apache Iceberg?
    If your team is managing large volumes of historical data using platforms like Snowflake, Amazon Redshift, or Google BigQuery, you’ve probably noticed a shift happening in the data engineering world. A new generation of data infrastructure is forming — one that prioritizes openness, interoperability, and cost-efficiency. At the center of that shift is Apache Iceberg. - Source: dev.to / about 1 month ago
  • Everyone Uses Postgres… But Why?
    Postgres can be easily adapted to build highly tailored solutions. For instance, Amazon Redshift can be considered a highly scalable fork of Postgres. It’s a distributed database focusing on OLAP workloads that you can deploy in AWS. - Source: dev.to / 6 months ago
  • From ETL and ELT to Reverse ETL
    With the transition from ETL to ELT, data warehouses have ascended to the role of data custodians, centralizing customer data collected from fragmented systems. This pivotal shift has been enabled by a suite of powerful tools: Fivetran and Airbyte streamline the extraction and loading, DBT handles the transformation, and robust warehousing solutions like Snowflake and Redshift store the data. While traditionally... - Source: dev.to / 7 months ago
  • Choosing Between a Streaming Database and a Stream Processing Framework in Python
    They differ from conventional analytic databases like Snowflake, Redshift, BigQuery, and Oracle in several ways. Conventional databases are batch-oriented, loading data in defined windows like hourly, daily, weekly, and so on. While loading data, conventional databases lock the tables, making the newly loaded data unavailable until the batch load is fully completed. Streaming databases continuously receive new... - Source: dev.to / about 1 year ago
  • Choosing the Right AWS Database: A Guide for Modern Applications
    Data warehousing is the process of storing and analyzing large volumes of data for business intelligence and analytics purposes. AWS offers a fully managed data warehousing service called Amazon Redshift that can handle petabyte-scale data warehouses with ease. - Source: dev.to / over 1 year ago
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What are some alternatives?

When comparing Apache Kylin and Amazon Redshift, you can also consider the following products

Spring Batch - Level up your Java code and explore what Spring can do for you.

Google BigQuery - A fully managed data warehouse for large-scale data analytics.

Microsoft SQL Server - Microsoft Azure is an open, flexible, enterprise-grade cloud computing platform. Move faster, do more, and save money with IaaS + PaaS. Try for FREE.

Apache Druid - Fast column-oriented distributed data store

Vertica - Vertica is a grid-based, column-oriented database designed to manage large, fast-growing volumes of...

Snowflakepowe.red - Snowflake Computing is delivering a data warehouse for the cloud.