Apache Spark

Website

spark.apache.org

$ Details

Categories

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

Apache ORC

Website

orc.apache.org

$ Details

Categories

#Big Data #Data Management #Data Dashboard #Data Warehousing

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.

Apache ORC features and specs

• Efficient Compression
  ORC provides highly efficient compression, which reduces the storage footprint of data and enhances performance by decreasing I/O operations.
• Columnar Storage
  The columnar storage format significantly improves read performance by allowing for selective access to necessary columns while ignoring others.
• Predicate Pushdown
  ORC supports predicate pushdown, enabling the query engine to skip over non-relevant data, thus enhancing query performance.
• Type Richness
  ORC supports complex types (like structs and maps), making it suitable for diverse data storage and query needs.
• Schema Evolution
  It facilitates seamless schema evolution, allowing easier adjustments to the dataset over time without breaking existing queries.
• Built-in Indexes
  Indexes such as bloom filters and min/max values are built-in, accelerating query processing by enabling quicker data lookup.

Possible disadvantages of Apache ORC

• Complexity
  The intricacies of its features may introduce additional complexity in implementation and maintenance, potentially increasing the learning curve.
• Write Performance
  While ORC is optimized for read-heavy workloads, its write performance can be less efficient compared to other formats like Parquet.
• Compatibility
  ORC may not be as widely supported as other formats, limiting the choice of tools and environments that can leverage its full capabilities.
• Compression Overhead
  The process of compressing and decompressing data can introduce a computational overhead, affecting performance in some scenarios.

Analysis of Apache Spark

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

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