Amazon ElastiCache vs Apache Spark

Overview

Amazon ElastiCache

Stacks1.3K

Followers1.0K

Votes151

Apache Spark

Stacks3.1K

Followers3.5K

Votes140

GitHub Stars42.2K

Forks28.9K

Amazon ElastiCache vs Apache Spark: What are the differences?

What is Amazon ElastiCache? Deploy, operate, and scale an in-memory cache in the cloud. ElastiCache improves the performance of web applications by allowing you to retrieve information from fast, managed, in-memory caches, instead of relying entirely on slower disk-based databases. ElastiCache supports Memcached and Redis.

What is Apache Spark? Fast and general engine for large-scale data processing. Spark is a fast and general processing engine compatible with Hadoop data. It can run in Hadoop clusters through YARN or Spark's standalone mode, and it can process data in HDFS, HBase, Cassandra, Hive, and any Hadoop InputFormat. It is designed to perform both batch processing (similar to MapReduce) and new workloads like streaming, interactive queries, and machine learning.

Amazon ElastiCache belongs to "Managed Memcache" category of the tech stack, while Apache Spark can be primarily classified under "Big Data Tools".

Some of the features offered by Amazon ElastiCache are:

Support for two engines: Memcached and Redis
Ease of management via the AWS Management Console. With a few clicks you can configure and launch instances for the engine you wish to use.
Compatibility with the specific engine protocol. This means most of the client libraries will work with the respective engines they were built for - no additional changes or tweaking required.

On the other hand, Apache Spark provides the following key features:

Run programs up to 100x faster than Hadoop MapReduce in memory, or 10x faster on disk
Write applications quickly in Java, Scala or Python
Combine SQL, streaming, and complex analytics

"Redis" is the primary reason why developers consider Amazon ElastiCache over the competitors, whereas "Open-source" was stated as the key factor in picking Apache Spark.

Apache Spark is an open source tool with 22.3K GitHub stars and 19.3K GitHub forks. Here's a link to Apache Spark's open source repository on GitHub.

According to the StackShare community, Amazon ElastiCache has a broader approval, being mentioned in 342 company stacks & 79 developers stacks; compared to Apache Spark, which is listed in 263 company stacks and 111 developer stacks.

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Advice on Amazon ElastiCache, Apache Spark

Nilesh

Technical Architect at Self Employed

Jul 8, 2020

Needs adviceon

Elasticsearch

Kafka

We have a Kafka topic having events of type A and type B. We need to perform an inner join on both type of events using some common field (primary-key). The joined events to be inserted in Elasticsearch.

In usual cases, type A and type B events (with same key) observed to be close upto 15 minutes. But in some cases they may be far from each other, lets say 6 hours. Sometimes event of either of the types never come.

In all cases, we should be able to find joined events instantly after they are joined and not-joined events within 15 minutes.

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Detailed Comparison

Amazon ElastiCache	Apache Spark
ElastiCache improves the performance of web applications by allowing you to retrieve information from fast, managed, in-memory caches, instead of relying entirely on slower disk-based databases. ElastiCache supports Memcached and Redis.	Spark is a fast and general processing engine compatible with Hadoop data. It can run in Hadoop clusters through YARN or Spark's standalone mode, and it can process data in HDFS, HBase, Cassandra, Hive, and any Hadoop InputFormat. It is designed to perform both batch processing (similar to MapReduce) and new workloads like streaming, interactive queries, and machine learning.
Support for two engines: Memcached and Redis;Ease of management via the AWS Management Console. With a few clicks you can configure and launch instances for the engine you wish to use.;Compatibility with the specific engine protocol. This means most of the client libraries will work with the respective engines they were built for - no additional changes or tweaking required.;Detailed monitoring statistics for the engine nodes at no extra cost via Amazon CloudWatch;Pay only for the resources you consume based on node hours used	Run programs up to 100x faster than Hadoop MapReduce in memory, or 10x faster on disk;Write applications quickly in Java, Scala or Python;Combine SQL, streaming, and complex analytics;Spark runs on Hadoop, Mesos, standalone, or in the cloud. It can access diverse data sources including HDFS, Cassandra, HBase, S3
Statistics
GitHub Stars -	GitHub Stars 42.2K
GitHub Forks -	GitHub Forks 28.9K
Stacks 1.3K	Stacks 3.1K
Followers 1.0K	Followers 3.5K
Votes 151	Votes 140
Pros & Cons
Pros 58 Redis 32 High-performance 26 Backed by amazon 21 Memcached 14 Elastic	Pros 61 Open-source 48 Fast and Flexible 8 One platform for every big data problem 8 Great for distributed SQL like applications 6 Easy to install and to use Cons 4 Speed

What are some alternatives to Amazon ElastiCache, Apache Spark?

Presto

Distributed SQL Query Engine for Big Data

Amazon Athena

Amazon Athena is an interactive query service that makes it easy to analyze data in Amazon S3 using standard SQL. Athena is serverless, so there is no infrastructure to manage, and you pay only for the queries that you run.

MemCachier

MemCachier provides an easy and powerful managed caching solution for all your performance and scalability needs. It works with the ubiquitous memcache protocol so your favourite language and framework already supports it.

Apache Flink

Apache Flink is an open source system for fast and versatile data analytics in clusters. Flink supports batch and streaming analytics, in one system. Analytical programs can be written in concise and elegant APIs in Java and Scala.

lakeFS

It is an open-source data version control system for data lakes. It provides a “Git for data” platform enabling you to implement best practices from software engineering on your data lake, including branching and merging, CI/CD, and production-like dev/test environments.

Druid

Druid is a distributed, column-oriented, real-time analytics data store that is commonly used to power exploratory dashboards in multi-tenant environments. Druid excels as a data warehousing solution for fast aggregate queries on petabyte sized data sets. Druid supports a variety of flexible filters, exact calculations, approximate algorithms, and other useful calculations.

Apache Kylin

Apache Kylin™ is an open source Distributed Analytics Engine designed to provide SQL interface and multi-dimensional analysis (OLAP) on Hadoop/Spark supporting extremely large datasets, originally contributed from eBay Inc.

Memcached Cloud

Memcached Cloud is a fully-managed service for running your Memcached in a reliable and fail-safe manner. Your dataset is constantly replicated, so if a node fails, an auto-switchover mechanism guarantees data is served without interruption. Memcached Cloud provides various data persistence options as well as remote backups for disaster recovery purposes.

Splunk

It provides the leading platform for Operational Intelligence. Customers use it to search, monitor, analyze and visualize machine data.

Apache Impala

Impala is a modern, open source, MPP SQL query engine for Apache Hadoop. Impala is shipped by Cloudera, MapR, and Amazon. With Impala, you can query data, whether stored in HDFS or Apache HBase – including SELECT, JOIN, and aggregate functions – in real time.

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