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Amazon EMR vs Xplenty: What are the differences?

Amazon EMR: Distribute your data and processing across a Amazon EC2 instances using Hadoop. Amazon EMR is used in a variety of applications, including log analysis, web indexing, data warehousing, machine learning, financial analysis, scientific simulation, and bioinformatics. Customers launch millions of Amazon EMR clusters every year; Xplenty: Code-free data integration, data transformation and ETL in the cloud. Read and process data from cloud storage sources such as Amazon S3, Rackspace Cloud Files and IBM SoftLayer Object Storage. Once done processing, Xplenty allows you to connect with Amazon Redshift, SAP HANA and Google BigQuery. You can also store processed data back in your favorite relational database, cloud storage or key-value store.

Amazon EMR and Xplenty can be primarily classified as "Big Data as a Service" tools.

Some of the features offered by Amazon EMR are:

Elastic- Amazon EMR enables you to quickly and easily provision as much capacity as you need and add or remove capacity at any time. Deploy multiple clusters or resize a running cluster
Low Cost- Amazon EMR is designed to reduce the cost of processing large amounts of data. Some of the features that make it low cost include low hourly pricing, Amazon EC2 Spot integration, Amazon EC2 Reserved Instance integration, elasticity, and Amazon S3 integration.
Flexible Data Stores- With Amazon EMR, you can leverage multiple data stores, including Amazon S3, the Hadoop Distributed File System (HDFS), and Amazon DynamoDB.

On the other hand, Xplenty provides the following key features:

Xplenty provides you with an visual, intuitive interface to design your ETL data flows
Xplenty lets you integrate data from a variety of data stores, such as Amazon RDS, MySQL, PostgreSQL, Microsoft SQL Server and MongoDB.
Read and process data from cloud storage sources such as Amazon S3, Rackspace Cloud Files and IBM SoftLayer Object Storage

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What is Amazon EMR?

Amazon EMR is used in a variety of applications, including log analysis, web indexing, data warehousing, machine learning, financial analysis, scientific simulation, and bioinformatics. Customers launch millions of Amazon EMR clusters every year.

What is Xplenty?

Read and process data from cloud storage sources such as Amazon S3, Rackspace Cloud Files and IBM SoftLayer Object Storage. Once done processing, Xplenty allows you to connect with Amazon Redshift, SAP HANA and Google BigQuery. You can also store processed data back in your favorite relational database, cloud storage or key-value store.

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Why do developers choose Amazon EMR?

Why do developers choose Xplenty?

14
On demand processing power
11
Don't need to maintain Hadoop Cluster yourself
7
Hadoop Tools
4
Elastic
4
Backed by Amazon
3
Economic - pay as you go, easy to use CLI and SDKs
2
Flexible

2
Simple, easy to integrate/process data without coding

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What are the cons of using Amazon EMR?

What are the cons of using Xplenty?

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What companies use Amazon EMR?

What companies use Xplenty?

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What tools integrate with Amazon EMR?

What tools integrate with Xplenty?

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What are some alternatives to Amazon EMR and Xplenty?

Amazon EC2

Amazon Elastic Compute Cloud (Amazon EC2) is a web service that provides resizable compute capacity in the cloud. It is designed to make web-scale computing easier for developers.

Amazon DynamoDB

All data items are stored on Solid State Drives (SSDs), and are replicated across 3 Availability Zones for high availability and durability. With DynamoDB, 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.

Hadoop

The Apache Hadoop software library is a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage.

Amazon Redshift

Redshift makes it simple and cost-effective to efficiently analyze all your data using your existing business intelligence tools. It is optimized for datasets 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.

Google BigQuery

Run super-fast, SQL-like queries against terabytes of data in seconds, using the processing power of Google's infrastructure. Load data with ease. Bulk load your data using Google Cloud Storage or stream it in. Easy access. Access BigQuery by using a browser tool, a command-line tool, or by making calls to the BigQuery REST API with client libraries such as Java, PHP or Python.

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