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- Apache Spark has changed how organizations deal with data management and its subsequent analytics. Spark, designed to get over the limitations of Hadoop MapReduce, provides in-memory computing capabilities that have set a new paradigm in terms of speed and efficiency. Businesses now rely on Spark for batch processing and instantaneous analytics.
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Why is Apache Spark so popular?
What is Apache Spark & how does it work?
Why is Apache Spark better than Hadoop?
What is sparksql & how does it work?
Why should you use Apache Spark for graph and machine-learning analytics?
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Apr 21, 2018 · 1. The big data marketplace is growing big every other day. The competitive struggle has reached an all new level. This is why open source technologies like Hadoop, Spark, and Flink must...
- Apache Spark: A Primer on Why Spark Matters and How It Works
In this article, we’ve explored why Apache Spark has become...
- Apache Spark: A Primer on Why Spark Matters and How It Works
May 13, 2024 · In this article, we’ve explored why Apache Spark has become the de facto standard for big data processing and how its architecture enables fast and efficient data analytics.
Aug 19, 2023 · Apache Spark is a powerful analytics engine, with support for SQL queries, machine learning, stream analysis, and graph processing. Spark is very efficient, with fast performance and low latency, due to its optimized design.
- Linode
- 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
Jun 29, 2024 · Why Enterprise Level Data Engineering With Apache Spark as Compute in 2024 Apache Spark is the most widely-used engine for scalable computing. Thousands of companies, including 80% of the...
- Daniel Mantovani
Jan 12, 2020 · Why would you want to use Spark? Spark has some big pros: High speed data querying, analysis, and transformation with large data sets.
Apr 3, 2024 · Models can be trained by data scientists in Apache Spark using R or Python, saved using MLlib, and then imported into a Java-based or Scala-based pipeline for production use.
