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Apache Spark VS Pentaho Data Integration

Compare Apache Spark VS Pentaho Data Integration and see what are their differences

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Apache Spark logo Apache Spark

Apache Spark is an engine for big data processing, with built-in modules for streaming, SQL, machine learning and graph processing.

Pentaho Data Integration logo Pentaho Data Integration

Hitachi Vantara brings Pentaho Data Integration, an end-to-end platform for all data integration challenges, that simplifies creation of data pipelines and provides big data processing.
  • Apache Spark Landing page
    Landing page //
    2021-12-31
  • Pentaho Data Integration Landing page
    Landing page //
    2023-05-08

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.

Pentaho Data Integration features and specs

  • User-Friendly Interface
    Pentaho Data Integration offers an intuitive drag-and-drop interface that simplifies the ETL process, making it accessible even for users without extensive technical expertise.
  • Extensive Connectivity
    Pentaho supports a wide range of data sources, including relational databases, NoSQL databases, cloud services, and big data platforms, providing flexibility for integration needs.
  • Scalability
    The platform can handle large volumes of data, making it suitable for enterprise-level data integration tasks and supporting growth in data needs over time.
  • Open-Source Community
    As an open-source tool, Pentaho benefits from a large and active community that contributes to its continuous improvement and provides a wealth of shared resources and plugins.
  • Integration with BI Tools
    Pentaho Data Integration seamlessly integrates with Pentaho's business intelligence tools, allowing for streamlined workflow from data ingestion to analytics and reporting.

Possible disadvantages of Pentaho Data Integration

  • Learning Curve
    While the interface is user-friendly, mastering the full capabilities of Pentaho can take time, especially for users new to ETL processes and data integration.
  • Performance Issues
    Some users report performance bottlenecks, especially when dealing with very large datasets or complex transformations, which may require additional optimization.
  • Limited Advanced Features
    Compared to some commercial ETL tools, Pentaho might lack certain advanced features, requiring additional customization or third-party solutions to fulfill complex requirements.
  • Documentation Quality
    The quality and depth of official documentation can sometimes be lacking, leading users to rely on community forums and external sources for troubleshooting.
  • Enterprise Edition Costs
    While the community edition of Pentaho is free, accessing the full suite of enterprise features and support requires a commercial license, which may be costly for some organizations.

Analysis of Apache Spark

Overall verdict

  • Yes, Apache Spark is generally considered good, especially for organizations and individuals that require efficient and fast data processing capabilities. It is well-supported, frequently updated, and widely adopted in the industry, making it a reliable choice for big data solutions.

Why this product is good

  • Apache Spark is highly valued because it provides a fast and general-purpose cluster-computing framework for big data processing. It offers extensive libraries for SQL, streaming, machine learning, and graph processing, making it versatile for various data processing needs. Its in-memory computing capability boosts the processing speed significantly compared to traditional disk-based processing. Additionally, Spark integrates well with Hadoop and other big data tools, providing a seamless ecosystem for large-scale data analysis.

Recommended for

  • Data scientists and engineers working with large datasets.
  • Organizations leveraging machine learning and analytics for decision-making.
  • Businesses needing real-time data processing capabilities.
  • Developers looking to integrate with Hadoop ecosystems.
  • Teams requiring robust support for multiple data sources and formats.

Apache Spark videos

+ Add

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

More videos:

  • Review - What's New in Apache Spark 3.0.0
  • Review - Apache Spark for Data Engineering and Analysis - Overview

Pentaho Data Integration videos

+ Add

pentaho Data Integration review

Category Popularity

0-100% (relative to Apache Spark and Pentaho Data Integration)

Apache Spark

Pentaho Data Integration

Databases
100 100%
Databases
0% 0
Backup & Sync
0 0%
Backup & Sync
100% 100
Big Data
100 100%
Big Data
0% 0
Data Integration
0 0%
Data Integration
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 Spark and Pentaho Data Integration

Apache Spark Reviews

15 data science tools to consider using in 2021
Apache Spark is an open source data processing and analytics engine that can handle large amounts of data -- upward of several petabytes, according to proponents. Spark's ability to rapidly process data has fueled significant growth in the use of the platform since it was created in 2009, helping to make the Spark project one of the largest open source communities among big...
Source: searchbusinessanalytics.techtarget.com
Top 15 Kafka Alternatives Popular In 2021
Apache Spark is a well-known, general-purpose, open-source analytics engine for large-scale, core data processing. It is known for its high-performance quality for data processing – batch and streaming with the help of its DAG scheduler, query optimizer, and engine. Data streams are processed in real-time and hence it is quite fast and efficient. Its machine learning...
Source: www.spec-india.com
5 Best-Performing Tools that Build Real-Time Data Pipeline
Apache Spark is an open-source and flexible in-memory framework which serves as an alternative to map-reduce for handling batch, real-time analytics and data processing workloads. It provides native bindings for the Java, Scala, Python, and R programming languages, and supports SQL, streaming data, machine learning and graph processing. From its beginning in the AMPLab at...
Source: www.analyticsinsight.net

Pentaho Data Integration Reviews

A List of The 16 Best ETL Tools And Why To Choose Them
In conclusion, there are many different ETL and data integration tools available, each with its own unique features and capabilities. Some popular options include SSIS, Talend Open Studio, Pentaho Data Integration, Hadoop, Airflow, AWS Data Pipeline, Google Dataflow, SAP BusinessObjects Data Services, and Hevo. Companies considering these tools should carefully evaluate...
Source: www.datacamp.com
15 Best ETL Tools in 2022 (A Complete Updated List)
Pentaho Data Integration enables the user to cleanse and prepare the data from various sources and allows the migration of data between applications. PDI is an open-source tool and is a part of the Pentaho business intelligent suite.
Source: www.softwaretestinghelp.com

Social recommendations and mentions

Based on our record, Apache Spark seems to be more popular. It has been mentiond 72 times since March 2021. 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 Spark mentions (72)

  • Gravitino - the unified metadata lake
    In the meantime, other query engine support is on the roadmap, including Apache Spark, Apache Flink, and others. - Source: dev.to / about 2 months ago
  • Introducing RisingWave's Hosted Iceberg Catalog-No External Setup Needed
    Because the hosted catalog is a standard JDBC catalog, tools like Spark, Trino, and Flink can still access your tables. For example:. - Source: dev.to / 3 months ago
  • Every Database Will Support Iceberg — Here's Why
    Apache Iceberg defines a table format that separates how data is stored from how data is queried. Any engine that implements the Iceberg integration — Spark, Flink, Trino, DuckDB, Snowflake, RisingWave — can read and/or write Iceberg data directly. - Source: dev.to / 5 months ago
  • How to Reduce Big Data Analytics Costs by 90% with Karpenter and Spark
    Apache Spark powers large-scale data analytics and machine learning, but as workloads grow exponentially, traditional static resource allocation leads to 30–50% resource waste due to idle Executors and suboptimal instance selection. - Source: dev.to / 6 months ago
  • Unveiling the Apache License 2.0: A Deep Dive into Open Source Freedom
    One of the key attributes of Apache License 2.0 is its flexible nature. Permitting use in both proprietary and open source environments, it has become the go-to choice for innovative projects ranging from the Apache HTTP Server to large-scale initiatives like Apache Spark and Hadoop. This flexibility is not solely legal; it is also philosophical. The license is designed to encourage transparency and maintain a... - Source: dev.to / 7 months ago
View more

Pentaho Data Integration mentions (0)

We have not tracked any mentions of Pentaho Data Integration yet. Tracking of Pentaho Data Integration recommendations started around Mar 2021.

What are some alternatives?

When comparing Apache Spark and Pentaho Data Integration, you can also consider the following products

Apache Flink - Flink is a streaming dataflow engine that provides data distribution, communication, and fault tolerance for distributed computations.

SAP Data Services - SAP Data Services provides functionality for data integration, quality, cleansing, and more.

Hadoop - Open-source software for reliable, scalable, distributed computing

Striim - Striim provides an end-to-end, real-time data integration and streaming analytics platform.

Apache Hive - Apache Hive data warehouse software facilitates querying and managing large datasets residing in distributed storage.

Oracle Data Integrator - Oracle Data Integrator is a data integration platform that covers batch loads, to trickle-feed integration processes.

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