Amazon DynamoDB vs Amazon EMR

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

Amazon DynamoDB

3.7K
3.2K
+ 1
195
Amazon EMR

541
680
+ 1
54
Add tool

Amazon DynamoDB vs Amazon EMR: What are the differences?

Introduction Amazon DynamoDB and Amazon EMR are both data storage and processing services provided by Amazon Web Services (AWS). While they have some similarities, there are several key differences between the two that make them suitable for different use cases.

  1. Data Structure: DynamoDB is a NoSQL database service, while EMR is a distributed big data processing framework. DynamoDB stores data in a structured key-value format, allowing for fast and predictable performance. On the other hand, EMR is designed to process large amounts of unstructured and semi-structured data using tools like Apache Hadoop, Spark, and Hive.

  2. Scalability: DynamoDB is a fully managed service that automatically scales to handle the requested throughput capacity. It can handle millions of requests per second and provides seamless scalability without any manual intervention. EMR, on the other hand, allows you to provision a cluster with a specific number of compute instances to process your data. Scaling in EMR requires manual adjustments to the cluster size and configurations.

  3. Data Availability: DynamoDB offers built-in multi-region replication, allowing you to replicate your data across multiple AWS regions for enhanced availability and disaster recovery. With EMR, you need to manually configure and manage data replication if you require data availability across regions.

  4. Data Processing Options: DynamoDB provides limited data processing capabilities with features like filtering, projection, and basic aggregations. It is best suited for simple and low-latency data access patterns. EMR, on the other hand, offers a wide range of data processing options through the various big data processing frameworks it supports. This allows you to perform complex transformations, machine learning tasks, and analytics on large datasets.

  5. Cost Model: DynamoDB charges you based on the provisioned throughput capacity and the amount of data stored. The pricing is predictable and can be optimized based on your specific workload requirements. EMR, on the other hand, charges you based on the EC2 instances used in the cluster, storage costs, and other associated services. The cost of EMR can vary depending on the size and complexity of your data processing jobs.

  6. Use Case Fit: DynamoDB is suitable for applications that require simple and low-latency data access with predictable performance, such as real-time applications, gaming leaderboards, and session stores. EMR, on the other hand, is well-suited for big data processing and analytics use cases, where you need to process large volumes of data with various processing frameworks and perform complex data transformations.

In summary, Amazon DynamoDB is a NoSQL database service that provides fast and scalable key-value data storage, while Amazon EMR is a distributed big data processing framework that allows for processing and analysis of large datasets using various tools and frameworks. The choice between DynamoDB and EMR depends on your specific data storage and processing needs.

Get Advice from developers at your company using StackShare Enterprise. Sign up for StackShare Enterprise.
Learn More
Pros of Amazon DynamoDB
Pros of Amazon EMR
  • 62
    Predictable performance and cost
  • 56
    Scalable
  • 35
    Native JSON Support
  • 21
    AWS Free Tier
  • 7
    Fast
  • 3
    No sql
  • 3
    To store data
  • 2
    Serverless
  • 2
    No Stored procedures is GOOD
  • 1
    ORM with DynamoDBMapper
  • 1
    Elastic Scalability using on-demand mode
  • 1
    Elastic Scalability using autoscaling
  • 1
    DynamoDB Stream
  • 15
    On demand processing power
  • 12
    Don't need to maintain Hadoop Cluster yourself
  • 7
    Hadoop Tools
  • 6
    Elastic
  • 4
    Backed by Amazon
  • 3
    Flexible
  • 3
    Economic - pay as you go, easy to use CLI and SDKs
  • 2
    Don't need a dedicated Ops group
  • 1
    Massive data handling
  • 1
    Great support

Sign up to add or upvote prosMake informed product decisions

Cons of Amazon DynamoDB
Cons of Amazon EMR
  • 4
    Only sequential access for paginate data
  • 1
    Scaling
  • 1
    Document Limit Size
    Be the first to leave a con

    Sign up to add or upvote consMake informed product decisions

    What is Amazon DynamoDB?

    With it , you can offload the administrative burden of operating and scaling a highly available distributed database cluster, while paying a low price for only what you use.

    What is Amazon EMR?

    It is used in a variety of applications, including log analysis, data warehousing, machine learning, financial analysis, scientific simulation, and bioinformatics.

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

    Jobs that mention Amazon DynamoDB and Amazon EMR as a desired skillset
    What companies use Amazon DynamoDB?
    What companies use Amazon EMR?
    See which teams inside your own company are using Amazon DynamoDB or Amazon EMR.
    Sign up for StackShare EnterpriseLearn More

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

    What tools integrate with Amazon DynamoDB?
    What tools integrate with Amazon EMR?

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

    Blog Posts

    Aug 28 2019 at 3:10AM

    Segment

    PythonJavaAmazon S3+16
    7
    2551
    GitHubPythonNode.js+47
    54
    72280
    GitHubGitSlack+30
    27
    18273
    GitHubDockerAmazon EC2+23
    12
    6560
    GitHubPythonSlack+25
    7
    3148
    What are some alternatives to Amazon DynamoDB and Amazon EMR?
    Google Cloud Datastore
    Use a managed, NoSQL, schemaless database for storing non-relational data. Cloud Datastore automatically scales as you need it and supports transactions as well as robust, SQL-like queries.
    MongoDB
    MongoDB stores data in JSON-like documents that can vary in structure, offering a dynamic, flexible schema. MongoDB was also designed for high availability and scalability, with built-in replication and auto-sharding.
    Amazon SimpleDB
    Developers simply store and query data items via web services requests and Amazon SimpleDB does the rest. Behind the scenes, Amazon SimpleDB creates and manages multiple geographically distributed replicas of your data automatically to enable high availability and data durability. Amazon SimpleDB provides a simple web services interface to create and store multiple data sets, query your data easily, and return the results. Your data is automatically indexed, making it easy to quickly find the information that you need. There is no need to pre-define a schema or change a schema if new data is added later. And scale-out is as simple as creating new domains, rather than building out new servers.
    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.
    Amazon S3
    Amazon Simple Storage Service provides a fully redundant data storage infrastructure for storing and retrieving any amount of data, at any time, from anywhere on the web
    See all alternatives