Amazon ElastiCache vs Apache Spark

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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.

- No public GitHub repository available -

What is Amazon ElastiCache?

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?

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.
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    What are some alternatives to Amazon ElastiCache and Apache Spark?
    Redis
    Redis is an open source, BSD licensed, advanced key-value store. It is often referred to as a data structure server since keys can contain strings, hashes, lists, sets and sorted sets.
    Elasticsearch
    Elasticsearch is a distributed, RESTful search and analytics engine capable of storing data and searching it in near real time. Elasticsearch, Kibana, Beats and Logstash are the Elastic Stack (sometimes called the ELK Stack).
    Memcached
    Memcached is an in-memory key-value store for small chunks of arbitrary data (strings, objects) from results of database calls, API calls, or page rendering.
    Azure Redis Cache
    It perfectly complements Azure database services such as Cosmos DB. It provides a cost-effective solution to scale read and write throughput of your data tier. Store and share database query results, session states, static contents, and more using a common cache-aside pattern.
    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.
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    Decisions about Amazon ElastiCache and Apache Spark
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    How developers use Amazon ElastiCache and Apache Spark
    Avatar of Volkan Özçelik
    Volkan Özçelik uses Amazon ElastiCacheAmazon ElastiCache

    I use a micro elesticache instance as a shared session store between the Node.js clusters of dojo.zerotoherojs.com and nightly.zerotoherojs.com

    Avatar of Wei Chen
    Wei Chen uses Apache SparkApache Spark

    Spark is good at parallel data processing management. We wrote a neat program to handle the TBs data we get everyday.

    Avatar of Cloud Consultant
    Cloud Consultant uses Amazon ElastiCacheAmazon ElastiCache

    Audit the ElastiCache configurations for best practices and standards.

    Avatar of Binded
    Binded uses Amazon ElastiCacheAmazon ElastiCache

    We use ElastiCache to run Redis, which we use as a queue through Kue.

    Avatar of Ralic Lo
    Ralic Lo uses Apache SparkApache Spark

    Used Spark Dataframe API on Spark-R for big data analysis.

    Avatar of BrainFinance
    BrainFinance uses Apache SparkApache Spark

    As a part of big data machine learning stack (SMACK).

    Avatar of Kalibrr
    Kalibrr uses Apache SparkApache Spark

    We use Apache Spark in computing our recommendations.

    Avatar of Dotmetrics
    Dotmetrics uses Apache SparkApache Spark

    Big data analytics and nightly transformation jobs.

    Avatar of Instacart
    Instacart uses Amazon ElastiCacheAmazon ElastiCache

    We use Elasticache. Both Redis and Memchached.

    Avatar of Bùi Thanh
    Bùi Thanh uses Amazon ElastiCacheAmazon ElastiCache
    • Redis cluster for cache and session storage
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