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Apache Kylin vs AtScale: What are the differences?
# Introduction
1. **Architecture**: Apache Kylin is an OLAP (Online Analytical Processing) engine while AtScale is a semantic layer that sits atop existing databases and data storage systems.
2. **Data Source Support**: Apache Kylin supports Hadoop-based data warehouses like HDFS, Hive, HBase, and more, whereas AtScale can connect to various data sources such as BigQuery, Snowflake, Redshift, and more.
3. **Scalability**: Apache Kylin utilizes a distributed architecture to achieve scalability with support for hundreds of TBs or PBs of data, while AtScale provides a virtual multi-dimensional cube model that allows for quick analytics but has limitations on the size of data it can handle efficiently.
4. **Query Performance**: Apache Kylin utilizes pre-built OLAP cubes to accelerate query performance significantly, whereas AtScale optimizes query performance through its intelligent query routing and caching mechanism.
5. **Data Modeling**: Apache Kylin requires a predefined data model with cube building time, while AtScale allows for dynamic schema-on-read modeling without the need to pre-aggregate the data.
6. **Management Overheads**: Apache Kylin requires more management overhead in terms of cube refreshing, building, and maintenance compared to AtScale’s more automated approach to managing semantic layers.
In Summary, Apache Kylin and AtScale differ in architecture, data source support, scalability, query performance, data modeling, and management overheads.
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Learn MorePros of Apache Kylin
Pros of AtScale
Pros of Apache Kylin
- Star schema and snowflake schema support7
- Seamless BI integration5
- OLAP on Hadoop4
- Easy install3
- Sub-second latency on extreme large dataset3
- ANSI-SQL2
Pros of AtScale
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What is Apache Kylin?
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.
What is AtScale?
Its Virtual Data Warehouse delivers performance, security and agility to exceed the demands of modern-day operational analytics.
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What companies use AtScale?
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What tools integrate with Apache Kylin?
What tools integrate with AtScale?
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What are some alternatives to Apache Kylin and AtScale?
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
Distributed SQL Query Engine for Big Data
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.
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.
Clickhouse
It allows analysis of data that is updated in real time. It offers instant results in most cases: the data is processed faster than it takes to create a query.