Amazon RDS vs Apache Spark

Get Advice Icon

Need advice about which tool to choose?Ask the StackShare community!

Amazon RDS
Amazon RDS

5K
2.7K
+ 1
754
Apache Spark
Apache Spark

1K
819
+ 1
98
Add tool

Amazon RDS vs Apache Spark: What are the differences?

What is Amazon RDS? Set up, operate, and scale a relational database in the cloud. Amazon RDS gives you access to the capabilities of a familiar MySQL, Oracle or Microsoft SQL Server database engine. This means that the code, applications, and tools you already use today with your existing databases can be used with Amazon RDS. Amazon RDS automatically patches the database software and backs up your database, storing the backups for a user-defined retention period and enabling point-in-time recovery. You benefit from the flexibility of being able to scale the compute resources or storage capacity associated with your Database Instance (DB Instance) via a single API call.

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 RDS belongs to "SQL Database as a Service" category of the tech stack, while Apache Spark can be primarily classified under "Big Data Tools".

Some of the features offered by Amazon RDS are:

  • Pre-configured Parameters
  • Monitoring and Metrics
  • Automatic Software Patching

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

"Reliable failovers" is the top reason why over 163 developers like Amazon RDS, while over 45 developers mention "Open-source" as the leading cause for choosing Apache Spark.

Apache Spark is an open source tool with 22.5K GitHub stars and 19.4K GitHub forks. Here's a link to Apache Spark's open source repository on GitHub.

Airbnb, Netflix, and Coursera are some of the popular companies that use Amazon RDS, whereas Apache Spark is used by Uber Technologies, Slack, and Shopify. Amazon RDS has a broader approval, being mentioned in 1435 company stacks & 526 developers stacks; compared to Apache Spark, which is listed in 266 company stacks and 112 developer stacks.

- No public GitHub repository available -

What is Amazon RDS?

Amazon RDS gives you access to the capabilities of a familiar MySQL, Oracle or Microsoft SQL Server database engine. This means that the code, applications, and tools you already use today with your existing databases can be used with Amazon RDS. Amazon RDS automatically patches the database software and backs up your database, storing the backups for a user-defined retention period and enabling point-in-time recovery. You benefit from the flexibility of being able to scale the compute resources or storage capacity associated with your Database Instance (DB Instance) via a single API call.

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.
Get Advice Icon

Need advice about which tool to choose?Ask the StackShare community!

Why do developers choose Amazon RDS?
Why do developers choose Apache Spark?

Sign up to add, upvote and see more prosMake informed product decisions

    Be the first to leave a con
    What companies use Amazon RDS?
    What companies use Apache Spark?

    Sign up to get full access to all the companiesMake informed product decisions

    What tools integrate with Amazon RDS?
    What tools integrate with Apache Spark?

    Sign up to get full access to all the tool integrationsMake informed product decisions

    What are some alternatives to Amazon RDS and Apache Spark?
    Amazon Redshift
    It is optimized for data sets ranging from a few hundred gigabytes to a petabyte or more and costs less than $1,000 per terabyte per year, a tenth the cost of most traditional data warehousing solutions.
    Apache Aurora
    Apache Aurora is a service scheduler that runs on top of Mesos, enabling you to run long-running services that take advantage of Mesos' scalability, fault-tolerance, and resource isolation.
    MySQL
    The MySQL software delivers a very fast, multi-threaded, multi-user, and robust SQL (Structured Query Language) database server. MySQL Server is intended for mission-critical, heavy-load production systems as well as for embedding into mass-deployed software.
    Oracle
    Oracle Database is an RDBMS. An RDBMS that implements object-oriented features such as user-defined types, inheritance, and polymorphism is called an object-relational database management system (ORDBMS). Oracle Database has extended the relational model to an object-relational model, making it possible to store complex business models in a relational database.
    Heroku Postgres
    Heroku Postgres provides a SQL database-as-a-service that lets you focus on building your application instead of messing around with database management.
    See all alternatives
    Decisions about Amazon RDS and Apache Spark
    StackShare Editors
    StackShare Editors
    Presto
    Presto
    Apache Spark
    Apache Spark
    Hadoop
    Hadoop

    Around 2015, the growing use of Uber’s data exposed limitations in the ETL and Vertica-centric setup, not to mention the increasing costs. “As our company grew, scaling our data warehouse became increasingly expensive. To cut down on costs, we started deleting older, obsolete data to free up space for new data.”

    To overcome these challenges, Uber rebuilt their big data platform around Hadoop. “More specifically, we introduced a Hadoop data lake where all raw data was ingested from different online data stores only once and with no transformation during ingestion.”

    “In order for users to access data in Hadoop, we introduced Presto to enable interactive ad hoc user queries, Apache Spark to facilitate programmatic access to raw data (in both SQL and non-SQL formats), and Apache Hive to serve as the workhorse for extremely large queries.

    See more
    StackShare Editors
    StackShare Editors
    Presto
    Presto
    Apache Spark
    Apache Spark
    Hadoop
    Hadoop

    To improve platform scalability and efficiency, Uber transitioned from JSON to Parquet, and built a central schema service to manage schemas and integrate different client libraries.

    While the first generation big data platform was vulnerable to upstream data format changes, “ad hoc data ingestions jobs were replaced with a standard platform to transfer all source data in its original, nested format into the Hadoop data lake.”

    These platform changes enabled the scaling challenges Uber was facing around that time: “On a daily basis, there were tens of terabytes of new data added to our data lake, and our Big Data platform grew to over 10,000 vcores with over 100,000 running batch jobs on any given day.”

    See more
    StackShare Editors
    StackShare Editors
    Presto
    Presto
    Apache Spark
    Apache Spark
    Scala
    Scala
    MySQL
    MySQL
    Kafka
    Kafka

    Slack’s data team works to “provide an ecosystem to help people in the company quickly and easily answer questions about usage, so they can make better and data informed decisions.” To achieve that goal, that rely on a complex data pipeline.

    An in-house tool call Sqooper scrapes MySQL backups and pipe them to S3. Job queue and log data is sent to Kafka then persisted to S3 using an open source tool called Secor, which was created by Pinterest.

    For compute, Amazon’s Elastic MapReduce (EMR) creates clusters preconfigured for Presto, Hive, and Spark.

    Presto is then used for ad-hoc questions, validating data assumptions, exploring smaller datasets, and creating visualizations for some internal tools. Hive is used for larger data sets or longer time series data, and Spark allows teams to write efficient and robust batch and aggregation jobs. Most of the Spark pipeline is written in Scala.

    Thrift binds all of these engines together with a typed schema and structured data.

    Finally, the Hive Metastore serves as the ground truth for all data and its schema.

    See more
    Tim Specht
    Tim Specht
    ‎Co-Founder and CTO at Dubsmash · | 13 upvotes · 57.5K views
    atDubsmashDubsmash
    Amazon RDS for Aurora
    Amazon RDS for Aurora
    Redis
    Redis
    Amazon DynamoDB
    Amazon DynamoDB
    Amazon RDS
    Amazon RDS
    Heroku
    Heroku
    PostgreSQL
    PostgreSQL
    #PlatformAsAService
    #Databases
    #NosqlDatabaseAsAService
    #SqlDatabaseAsAService

    Over the years we have added a wide variety of different storages to our stack including PostgreSQL (some hosted by Heroku, some by Amazon RDS) for storing relational data, Amazon DynamoDB to store non-relational data like recommendations & user connections, or Redis to hold pre-aggregated data to speed up API endpoints.

    Since we started running Postgres ourselves on RDS instead of only using the managed offerings of Heroku, we've gained additional flexibility in scaling our application while reducing costs at the same time.

    We are also heavily testing Amazon RDS for Aurora in its Postgres-compatible version and will also give the new release of Aurora Serverless a try!

    #SqlDatabaseAsAService #NosqlDatabaseAsAService #Databases #PlatformAsAService

    See more
    StackShare Editors
    StackShare Editors
    Apache Thrift
    Apache Thrift
    Kotlin
    Kotlin
    Presto
    Presto
    HHVM (HipHop Virtual Machine)
    HHVM (HipHop Virtual Machine)
    gRPC
    gRPC
    Kubernetes
    Kubernetes
    Apache Spark
    Apache Spark
    Airflow
    Airflow
    Terraform
    Terraform
    Hadoop
    Hadoop
    Swift
    Swift
    Hack
    Hack
    Memcached
    Memcached
    Consul
    Consul
    Chef
    Chef
    Prometheus
    Prometheus

    Since the beginning, Cal Henderson has been the CTO of Slack. Earlier this year, he commented on a Quora question summarizing their current stack.

    Apps
    • Web: a mix of JavaScript/ES6 and React.
    • Desktop: And Electron to ship it as a desktop application.
    • Android: a mix of Java and Kotlin.
    • iOS: written in a mix of Objective C and Swift.
    Backend
    • The core application and the API written in PHP/Hack that runs on HHVM.
    • The data is stored in MySQL using Vitess.
    • Caching is done using Memcached and MCRouter.
    • The search service takes help from SolrCloud, with various Java services.
    • The messaging system uses WebSockets with many services in Java and Go.
    • Load balancing is done using HAproxy with Consul for configuration.
    • Most services talk to each other over gRPC,
    • Some Thrift and JSON-over-HTTP
    • Voice and video calling service was built in Elixir.
    Data warehouse
    • Built using open source tools including Presto, Spark, Airflow, Hadoop and Kafka.
    Etc
    See more
    Julien DeFrance
    Julien DeFrance
    Principal Software Engineer at Tophatter · | 16 upvotes · 384.3K views
    atSmartZipSmartZip
    Amazon DynamoDB
    Amazon DynamoDB
    Ruby
    Ruby
    Node.js
    Node.js
    AWS Lambda
    AWS Lambda
    New Relic
    New Relic
    Amazon Elasticsearch Service
    Amazon Elasticsearch Service
    Elasticsearch
    Elasticsearch
    Superset
    Superset
    Amazon Quicksight
    Amazon Quicksight
    Amazon Redshift
    Amazon Redshift
    Zapier
    Zapier
    Segment
    Segment
    Amazon CloudFront
    Amazon CloudFront
    Memcached
    Memcached
    Amazon ElastiCache
    Amazon ElastiCache
    Amazon RDS for Aurora
    Amazon RDS for Aurora
    MySQL
    MySQL
    Amazon RDS
    Amazon RDS
    Amazon S3
    Amazon S3
    Docker
    Docker
    Capistrano
    Capistrano
    AWS Elastic Beanstalk
    AWS Elastic Beanstalk
    Rails API
    Rails API
    Rails
    Rails
    Algolia
    Algolia

    Back in 2014, I was given an opportunity to re-architect SmartZip Analytics platform, and flagship product: SmartTargeting. This is a SaaS software helping real estate professionals keeping up with their prospects and leads in a given neighborhood/territory, finding out (thanks to predictive analytics) who's the most likely to list/sell their home, and running cross-channel marketing automation against them: direct mail, online ads, email... The company also does provide Data APIs to Enterprise customers.

    I had inherited years and years of technical debt and I knew things had to change radically. The first enabler to this was to make use of the cloud and go with AWS, so we would stop re-inventing the wheel, and build around managed/scalable services.

    For the SaaS product, we kept on working with Rails as this was what my team had the most knowledge in. We've however broken up the monolith and decoupled the front-end application from the backend thanks to the use of Rails API so we'd get independently scalable micro-services from now on.

    Our various applications could now be deployed using AWS Elastic Beanstalk so we wouldn't waste any more efforts writing time-consuming Capistrano deployment scripts for instance. Combined with Docker so our application would run within its own container, independently from the underlying host configuration.

    Storage-wise, we went with Amazon S3 and ditched any pre-existing local or network storage people used to deal with in our legacy systems. On the database side: Amazon RDS / MySQL initially. Ultimately migrated to Amazon RDS for Aurora / MySQL when it got released. Once again, here you need a managed service your cloud provider handles for you.

    Future improvements / technology decisions included:

    Caching: Amazon ElastiCache / Memcached CDN: Amazon CloudFront Systems Integration: Segment / Zapier Data-warehousing: Amazon Redshift BI: Amazon Quicksight / Superset Search: Elasticsearch / Amazon Elasticsearch Service / Algolia Monitoring: New Relic

    As our usage grows, patterns changed, and/or our business needs evolved, my role as Engineering Manager then Director of Engineering was also to ensure my team kept on learning and innovating, while delivering on business value.

    One of these innovations was to get ourselves into Serverless : Adopting AWS Lambda was a big step forward. At the time, only available for Node.js (Not Ruby ) but a great way to handle cost efficiency, unpredictable traffic, sudden bursts of traffic... Ultimately you want the whole chain of services involved in a call to be serverless, and that's when we've started leveraging Amazon DynamoDB on these projects so they'd be fully scalable.

    See more
    Eric Colson
    Eric Colson
    Chief Algorithms Officer at Stitch Fix · | 19 upvotes · 270.6K views
    atStitch FixStitch Fix
    Amazon EC2 Container Service
    Amazon EC2 Container Service
    Docker
    Docker
    PyTorch
    PyTorch
    R
    R
    Python
    Python
    Presto
    Presto
    Apache Spark
    Apache Spark
    Amazon S3
    Amazon S3
    PostgreSQL
    PostgreSQL
    Kafka
    Kafka
    #Data
    #DataStack
    #DataScience
    #ML
    #Etl
    #AWS

    The algorithms and data infrastructure at Stitch Fix is housed in #AWS. Data acquisition is split between events flowing through Kafka, and periodic snapshots of PostgreSQL DBs. We store data in an Amazon S3 based data warehouse. Apache Spark on Yarn is our tool of choice for data movement and #ETL. Because our storage layer (s3) is decoupled from our processing layer, we are able to scale our compute environment very elastically. We have several semi-permanent, autoscaling Yarn clusters running to serve our data processing needs. While the bulk of our compute infrastructure is dedicated to algorithmic processing, we also implemented Presto for adhoc queries and dashboards.

    Beyond data movement and ETL, most #ML centric jobs (e.g. model training and execution) run in a similarly elastic environment as containers running Python and R code on Amazon EC2 Container Service clusters. The execution of batch jobs on top of ECS is managed by Flotilla, a service we built in house and open sourced (see https://github.com/stitchfix/flotilla-os).

    At Stitch Fix, algorithmic integrations are pervasive across the business. We have dozens of data products actively integrated systems. That requires serving layer that is robust, agile, flexible, and allows for self-service. Models produced on Flotilla are packaged for deployment in production using Khan, another framework we've developed internally. Khan provides our data scientists the ability to quickly productionize those models they've developed with open source frameworks in Python 3 (e.g. PyTorch, sklearn), by automatically packaging them as Docker containers and deploying to Amazon ECS. This provides our data scientist a one-click method of getting from their algorithms to production. We then integrate those deployments into a service mesh, which allows us to A/B test various implementations in our product.

    For more info:

    #DataScience #DataStack #Data

    See more
    Interest over time
    Reviews of Amazon RDS and Apache Spark
    No reviews found
    How developers use Amazon RDS and Apache Spark
    Avatar of Pathwright
    Pathwright uses Amazon RDSAmazon RDS

    While we initially started off running our own Postgres cluster, we evaluated RDS and found it to be an excellent fit for us.

    The failovers, manual scaling, replication, Postgres upgrades, and pretty much everything else has been super smooth and reliable.

    We'll probably need something a little more complex in the future, but RDS performs admirably for now.

    Avatar of AngeloR
    AngeloR uses Amazon RDSAmazon RDS

    We are using RDS for managing PostgreSQL and legacy MSSQL databases.

    Unfortunately while RDS works great for managing the PostgreSQL systems, MSSQL is very much a second class citizen and they don't offer very much capability. Infact, in order to upgrade instance storage for MSSQL we actually have to spin up a new cluster and migrate the data over.

    Avatar of Wirkn Inc.
    Wirkn Inc. uses Amazon RDSAmazon RDS

    Our PostgreSQL servers, where we keep the bulk of Wirkn data, are hosted on the fantastically easy and reliable AWS RDS platform.

    Avatar of Digital2Go
    Digital2Go uses Amazon RDSAmazon RDS

    We use Aurora for our OLTP database, it provides significant speed increases on top of MySQL without the need to manage it

    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 fadingdust
    fadingdust uses Amazon RDSAmazon RDS

    RDS allows us to replicate the development databases locally as well as making it available to CircleCI.

    Avatar of Ralic Lo
    Ralic Lo uses Apache SparkApache Spark

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

    Avatar of Kalibrr
    Kalibrr uses Apache SparkApache Spark

    We use Apache Spark in computing our recommendations.

    Avatar of BrainFinance
    BrainFinance uses Apache SparkApache Spark

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

    Avatar of Dotmetrics
    Dotmetrics uses Apache SparkApache Spark

    Big data analytics and nightly transformation jobs.

    How much does Amazon RDS cost?
    How much does Apache Spark cost?
    Pricing unavailable
    News about Apache Spark
    More news