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Computational speed, scalability, and programmability
- It is designed to deliver the computational speed, scalability, and programmability required for big data—specifically for streaming data, graph data, analytics, machine learning, large-scale data processing, and artificial intelligence (AI) applications.
www.ibm.com/topics/apache-spark
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What is spark & why should you use it?
Aug 12, 2024 · Apache Spark is a powerful open-source tool designed to handle big data processing. It’s known for its speed and ease of use, making it a favorite among data engineers and data scientists.
- An Introduction to Apache Spark: Big Data Processing Made ...
Apache Spark has revolutionized the world of big data...
- An Introduction to Apache Spark: Big Data Processing Made ...
Apache Spark is an open-source data-processing engine for large data sets, designed to deliver the speed, scalability and programmability required for big data.
- What Is Apache Spark? An Introduction
- Spark CORE
- SparkSQL
- Spark Streaming
- MLlib
- Graphx
- How to Use Apache Spark: Event Detection Use Case
- Other Apache Spark Use Cases
- Conclusion
Sparkis an Apache project advertised as “lightning fast cluster computing”. It has a thriving open-source community and is the most active Apache project at the moment. Spark provides a faster and more general data processing platform. Spark lets you run programs up to 100x faster in memory, or 10x faster on disk, than Hadoop. Last year, Spark took...
Spark Coreis the base engine for large-scale parallel and distributed data processing. It is responsible for: 1. memory management and fault recovery 2. scheduling, distributing and monitoring jobs on a cluster 3. interacting with storage systems Spark introduces the concept of an RDD (Resilient Distributed Dataset), an immutable fault-tolerant, di...
SparkSQL is a Spark component that supports querying data either via SQL or via the Hive Query Language. It originated as the Apache Hive port to run on top of Spark (in place of MapReduce) and is now integrated with the Spark stack. In addition to providing support for various data sources, it makes it possible to weave SQL queries with code trans...
Spark Streamingsupports real time processing of streaming data, such as production web server log files (e.g. Apache Flume and HDFS/S3), social media like Twitter, and various messaging queues like Kafka. Under the hood, Spark Streaming receives the input data streams and divides the data into batches. Next, they get processed by the Spark engine a...
MLlib is a machine learning library that provides various algorithms designed to scale out on a cluster for classification, regression, clustering, collaborative filtering, and so on (check out Toptal’s article on machine learning for more information on that topic). Some of these algorithms also work with streaming data, such as linear regression ...
GraphXis a library for manipulating graphs and performing graph-parallel operations. It provides a uniform tool for ETL, exploratory analysis and iterative graph computations. Apart from built-in operations for graph manipulation, it provides a library of common graph algorithms such as PageRank.
Now that we have answered the question “What is Apache Spark?”, let’s think of what kind of problems or challenges it could be used for most effectively. I came across an article recently about an experiment to detect an earthquake by analyzing a Twitter stream. Interestingly, it was shown that this technique was likely to inform you of an earthqua...
Potential use cases for Spark extend far beyond detection of earthquakes of course. Here’s a quick (but certainly nowhere near exhaustive!) sampling of other use cases that require dealing with the velocity, variety and volume of Big Data, for which Spark is so well suited: In the game industry, processing and discovering patterns from the potentia...
To sum up, Spark helps to simplify the challenging and computationally intensive task of processing high volumes of real-time or archived data, both structured and unstructured, seamlessly integrating relevant complex capabilities such as machine learning and graph algorithms. Spark brings Big Data processing to the masses. Check it out!
- Radek Ostrowski
Sep 15, 2024 · Apache Spark is a versatile fast and scalable solution for big data processing. Its ability to handle batch and real-time data processing along with support for machine learning and SQL queries makes it an essential tool for modern data engineering.
Apr 3, 2024 · Apache Spark is a data processing framework that can quickly perform processing tasks on very large data sets, and can also distribute data processing tasks across multiple computers, either...
- Ian Pointer
Apr 26, 2024 · RDD is the backbone of Apache Spark. It allows data to be stored in memory and enables faster data access and processing. Instead of reading and writing the data repeatedly from the disk, Spark processes the entire data in just memory.
May 29, 2023 · Apache Spark has revolutionized the world of big data processing, providing a fast, scalable, and versatile solution for handling large-scale data analytics tasks.
