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Apache Spark started as a research project at the UC Berkeley AMPLab in 2009, and was open sourced in early 2010. Many of the ideas behind the system were presented in various research papers over the years. After being released, Spark grew into a broad developer community, and moved to the Apache Software Foundation in 2013.
Apache Spark is an open-source unified analytics engine for large-scale data processing. Spark provides an interface for programming clusters with implicit data parallelism and fault tolerance.
- 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
Jan 8, 2024 · Apache Spark is an open-source cluster-computing framework. It provides elegant development APIs for Scala, Java, Python, and R that allow developers to execute a variety of data-intensive workloads across diverse data sources including HDFS, Cassandra, HBase, S3 etc.
Oct 15, 2015 · Spark is often used alongside Hadoop’s data storage module — HDFS — but it can integrate equally well with other popular data storage subsystems such as HBase, Cassandra, MapR-DB, MongoDB and...
Aug 3, 2023 · Apache Spark is the platform of choice due to its blazing data processing speed, ease-of-use, and fault tolerant features. In this article, we took a look at the architecture of Spark and what is the secret of its lightning-fast processing speed with the help of an example.
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Apr 3, 2024 · Fast, flexible, and developer-friendly, Apache Spark is the leading platform for large-scale SQL, batch processing, stream processing, and machine learning.
