Apache Kylin

Website

kylin.apache.org

$ Details

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Categories

#Databases #Big Data #Relational Databases #Data Warehousing

Apache Spark

Website

spark.apache.org

$ Details

Categories

#Databases #Big Data #Big Data Analytics #Big Data Infrastructure

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.

Apache Spark features and specs

• Speed
  Apache Spark processes data in-memory, significantly increasing the processing speed of data tasks compared to traditional disk-based engines.
• Ease of Use
  Spark offers high-level APIs in Java, Scala, Python, and R, making it accessible to a broad range of developers and data scientists.
• Advanced Analytics
  Spark supports advanced analytics, including machine learning, graph processing, and real-time streaming, which can be executed in the same application.
• Scalability
  Spark can handle both small- and large-scale data processing tasks, scaling seamlessly from a single machine to thousands of servers.
• Support for Various Data Sources
  Spark can integrate with a wide variety of data sources, including HDFS, Apache HBase, Apache Hive, Cassandra, and many others.
• Active Community
  Spark has a vibrant and active community, providing a wealth of extensions, tools, and support options.

Possible disadvantages of Apache Spark

• Memory Consumption
  Spark's in-memory processing can be resource-intensive, requiring substantial amounts of RAM, which can drive up costs for large-scale deployments.
• Complexity in Configuration
  To optimize performance, Spark requires careful configuration and tuning, which can be complex and time-consuming.
• Learning Curve
  Despite its ease of use, mastering the full range of Spark's features and best practices can take considerable time and effort.
• Latency for Small Data
  For smaller datasets or low-latency requirements, Spark might not be the most efficient choice, as other technologies could offer better performance.
• Integration Overhead
  Though Spark integrates with many systems, incorporating it into an existing data infrastructure can introduce additional overhead and complexity.
• Community Support Variability
  While the community is active, the support and quality of third-party libraries and tools can be inconsistent, leading to potential challenges in implementation.

Extreme OLAP Analytics with Apache Kylin - Big Data Application Meetup

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Weekly Apache Spark live Code Review -- look at StringIndexer multi-col (Scala) & Python testing

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