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FME by Safe VS Apache Spark

Compare FME by Safe VS Apache Spark and see what are their differences

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Contents:
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  2. » Videos
  3. » Reviews
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FME by Safe logo FME by Safe

FME is an integrated collection of Spatial ETL tools for data transformation and data translation.

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.
  • FME by Safe Landing page
    Landing page //
    2023-04-25
  • Apache Spark Landing page
    Landing page //
    2021-12-31

FME by Safe features and specs

No features have been listed yet.

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.

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.

FME by Safe videos

+ Add

https://engage.safe.com/training/recorded/

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

Category Popularity

0-100% (relative to FME by Safe and Apache Spark)

FME by Safe

Apache Spark

Big Data
10 10%
Big Data
90% 90
Databases
10 10%
Databases
90% 90
Data Warehousing
100 100%
Data Warehousing
0% 0
Stream Processing
0 0%
Stream Processing
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 FME by Safe and Apache Spark

FME by Safe Reviews

We have no reviews of FME by Safe yet.
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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

Social recommendations and mentions

Based on our record, Apache Spark seems to be a lot more popular than FME by Safe. While we know about 70 links to Apache Spark, we've tracked only 3 mentions of FME by Safe. 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.

FME by Safe mentions (3)

  • Where can I learn the basics of FME and ETL in GIS?
    Safe.com has all the resources you could ever imagine. Safe also offers a free at home license. Source: over 2 years ago
  • Best paid ELT tool for a startup
    We have had a lot of success with FME from safe.com. Covers all your reqs. You can get a decent free trail and have a cloud version also. Top of my head its around £800 per licence - alongside there ifra server. We are exploring FME cloud at the moment which will give us a PAYG option. Source: over 2 years ago
  • Everything about Google Chrome’s Latest Update: Version 90
    In the competition for (HTTP:// vs HTTPS://), HTTPS certainly wins as it is a safer navigation option for users. This implies if you visit a website let us say ‘safe.com’ on your browser, Google will load the website as https://safe.com. Here, Google discards the usual http://safe.com completely making the ‘HTTPS’ default. - Source: dev.to / almost 4 years ago

Apache Spark mentions (70)

  • 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 / about 1 month 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 / about 1 month 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 / 3 months ago
  • The Application of Java Programming In Data Analysis and Artificial Intelligence
    [1] S. Russell and P. Norvig, Artificial Intelligence: A Modern Approach. Pearson, 2020. [2] F. Chollet, Deep Learning with Python. Manning Publications, 2018. [3] C. C. Aggarwal, Data Mining: The Textbook. Springer, 2015. [4] J. Dean and S. Ghemawat, "MapReduce: Simplified Data Processing on Large Clusters," Communications of the ACM, vol. 51, no. 1, pp. 107-113, 2008. [5] Apache Software Foundation, "Apache... - Source: dev.to / 3 months ago
  • Automating Enhanced Due Diligence in Regulated Applications
    If you're designing an event-based pipeline, you can use a data streaming tool like Kafka to process data as it's collected by the pipeline. For a setup that already has data stored, you can use tools like Apache Spark to batch process and clean it before moving ahead with the pipeline. - Source: dev.to / 3 months ago
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What are some alternatives?

When comparing FME by Safe and Apache Spark, you can also consider the following products

Microsoft Azure Data Lake - Azure Data Lake is a real-time data processing and analytics solution that works across platforms and languages.

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

Amazon Redshift - Learn about Amazon Redshift cloud data warehouse.

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

Greenplum Database - Greenplum Database is an open source parallel data warehousing platform.

Apache Storm - Apache Storm is a free and open source distributed realtime computation system.

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