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Apache Spark

Apache Spark

Overview

Learn from top reviewers

Apache Spark is still a valid DE tool

Rating: 9 out of 10
December 28, 2024
IncentivizedVetted ReviewVerified User
We use Apache Spark on a daily basis as the main computation engine for updating most critical and non-critical data pipelines. We mostly work with batch processing but there ...
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Verified user
Employee
Apache Spark6 years of experience

Apache Spark: Lightning-Fast Distributed Computing with a Learning Curve

Rating: 10 out of 10
August 18, 2023
IncentivizedVetted ReviewVerified User
If you are working on large and big scale data with analytics - don't go further without the use of Apache Spark! One of the projects that I was involved in using Apache Spark...
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Ananth Gouri
Professional - Engineering
Assistant Professor
The National Institute of Engineering, Mysuru
Apache Spark4 years of experience

Lightning Fast In-Memory Cluster Computing Framework

Rating: 10 out of 10
August 30, 2022
Vetted ReviewVerified User
Earlier we were using RDBMS like Oracle for retail and eCommerce data. We faced challenges such as cost, performance, and a huge amount of transactions coming in. After a lot ...
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Riyaz Khan
Team Lead - Information Technology
Staff Engineer
Nagarro
Apache Spark5 years of experience

Apache Spark is the next generation of big data computing.

Rating: 9 out of 10
April 18, 2022
Vetted ReviewVerified User
We need to calculate risk-weighted assets (RWA) daily and monthly for different positions the bank holds on a T+1 basis. The volume of calculations is large: more than million...
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Steven Li
Consultant - Information Technology
Senior Software Developer (Consultant)
Morgan Stanley
Apache Spark1 year of experience

Apache Spark - your go to technology for distributed data processing

Rating: 9 out of 10
May 3, 2021
IncentivizedVetted ReviewVerified User
We are using Apache Spark in Digital - Data teams to build data products and help business teams to take data-driven decisions.We use Apache Spark to source that from differen...
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Surendranatha Reddy Chappidi
Engineer - Information Technology
Senior Data Engineer
A.P. Moller - Maersk
Apache Spark4 years of experience
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Product Demos

Spark Project | Spark Tutorial | Online Spark Training | Intellipaat

YouTube

Spark SQL Tutorial | Spark SQL Using Scala | Apache Spark Tutorial For Beginners | Simplilearn

YouTube

Apache Spark Full Course | Apache Spark Tutorial For Beginners | Learn Spark In 7 Hours |Simplilearn

YouTube

Apache Spark Architecture | Spark Cluster Architecture Explained | Spark Training | Edureka

YouTube

Introduction to Databricks [New demo linked in description]

YouTube

Apache Spark Tutorial | Spark Tutorial for Beginners | Spark Big Data | Intellipaat

YouTube
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Product Details

What is Apache Spark?

Apache Spark Technical Details

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Mobile ApplicationNo
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Comparisons

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Reviews From Top Reviewers

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(1-5 of 24)

Apache Spark is still a valid DE tool

Rating: 9 out of 10
Incentivized
December 28, 2024
Verified User
Employee in Engineering
Consumer Services Company, 1001-5000 employees
Vetted Review
Verified User
Apache Spark
6 years of experience
Pros
  • Parallel processing
  • Configurability
  • Usage with other tools
Cons
  • More ready-to-use solutions for tweaking the Apache Spark configs
  • Reduce the creation of UDFs for Pyspark by implementing transformations directly

Apache Spark: Lightning-Fast Distributed Computing with a Learning Curve

Rating: 10 out of 10
Incentivized
August 18, 2023
AG
Ananth Gouri
Assistant Professor
The National Institute of Engineering, Mysuru (Education Management, 501-1000 employees)
Vetted Review
Verified User
Apache Spark
4 years of experience
View profile
Pros
  • Fault-tolerant systems: in most cases, no node fails. If it fails - the processing still continues.
  • Scalable to any extent.
  • Has built-in machine learning library called - MLlib
  • Very flexible - data from various data sources can be used. Usage with HDFS is very easy
Cons
  • Its fully not backward compatible.
  • It is memory-consuming for heavy and large workloads and datasets
  • Support for advanced analytics is not available - MLlib has minimalistic analytics.
  • Deployment is a complex task for beginners.

Apache Spark is the next generation of big data computing.

Rating: 9 out of 10
April 18, 2022
SL
Steven Li
Senior Software Developer (Consultant)
Morgan Stanley (Banking, 10,001+ employees)
Vetted Review
Verified User
Apache Spark
1 year of experience
View profile
Pros
  • DataFrame as a distributed collection of data: easy for developers to implement algorithms and formulas.
  • Calculation in-memory.
  • Cluster to distribute large data of calculation.
Cons
  • It would be great if Apache Spark could provide a native database to manage all file info of saved parquet.

Apache Spark - your go to technology for distributed data processing

Rating: 9 out of 10
Incentivized
May 03, 2021
Pros
  • Spark is very fast compered to other frameworks because it works in cluster mode and use distributed processing and computation frameworks internally
  • Robust and fault tolerant
  • Open source
  • Can source data from multiple data sources
Cons
  • No Dataset API support in python version of spark
  • Apache Spark job run UI can have more meaningful information
  • Spark errors can provide more meaningful information when a job is failed
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