Amazon EMR vs Amazon Redshift vs Qubole

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

Amazon EMR

450
517
+ 1
54
Amazon Redshift

1.2K
1K
+ 1
103
Qubole

31
90
+ 1
65
Advice on Amazon EMR, Amazon Redshift, and Qubole

We need to perform ETL from several databases into a data warehouse or data lake. We want to

  • keep raw and transformed data available to users to draft their own queries efficiently
  • give users the ability to give custom permissions and SSO
  • move between open-source on-premises development and cloud-based production environments

We want to use inexpensive Amazon EC2 instances only on medium-sized data set 16GB to 32GB feeding into Tableau Server or PowerBI for reporting and data analysis purposes.

See more
Replies (3)

You could also use AWS Lambda and use Cloudwatch event schedule if you know when the function should be triggered. The benefit is that you could use any language and use the respective database client.

But if you orchestrate ETLs then it makes sense to use Apache Airflow. This requires Python knowledge.

See more
Recommends
Airflow

Though we have always built something custom, Apache airflow (https://airflow.apache.org/) stood out as a key contender/alternative when it comes to open sources. On the commercial offering, Amazon Redshift combined with Amazon Kinesis (for complex manipulations) is great for BI, though Redshift as such is expensive.

See more
Recommends

You may want to look into a Data Virtualization product called Conduit. It connects to disparate data sources in AWS, on prem, Azure, GCP, and exposes them as a single unified Spark SQL view to PowerBI (direct query) or Tableau. Allows auto query and caching policies to enhance query speeds and experience. Has a GPU query engine and optimized Spark for fallback. Can be deployed on your AWS VM or on prem, scales up and out. Sounds like the ideal solution to your needs.

See more
View all (3)
Decisions about Amazon EMR, Amazon Redshift, and Qubole
Julien Lafont

Cloud Data-warehouse is the centerpiece of modern Data platform. The choice of the most suitable solution is therefore fundamental.

Our benchmark was conducted over BigQuery and Snowflake. These solutions seem to match our goals but they have very different approaches.

BigQuery is notably the only 100% serverless cloud data-warehouse, which requires absolutely NO maintenance: no re-clustering, no compression, no index optimization, no storage management, no performance management. Snowflake requires to set up (paid) reclustering processes, to manage the performance allocated to each profile, etc. We can also mention Redshift, which we have eliminated because this technology requires even more ops operation.

BigQuery can therefore be set up with almost zero cost of human resources. Its on-demand pricing is particularly adapted to small workloads. 0 cost when the solution is not used, only pay for the query you're running. But quickly the use of slots (with monthly or per-minute commitment) will drastically reduce the cost of use. We've reduced by 10 the cost of our nightly batches by using flex slots.

Finally, a major advantage of BigQuery is its almost perfect integration with Google Cloud Platform services: Cloud functions, Dataflow, Data Studio, etc.

BigQuery is still evolving very quickly. The next milestone, BigQuery Omni, will allow to run queries over data stored in an external Cloud platform (Amazon S3 for example). It will be a major breakthrough in the history of cloud data-warehouses. Omni will compensate a weakness of BigQuery: transferring data in near real time from S3 to BQ is not easy today. It was even simpler to implement via Snowflake's Snowpipe solution.

We also plan to use the Machine Learning features built into BigQuery to accelerate our deployment of Data-Science-based projects. An opportunity only offered by the BigQuery solution

See more
Get Advice from developers at your company using Private StackShare. Sign up for Private StackShare.
Learn More
Pros of Amazon EMR
Pros of Amazon Redshift
Pros of Qubole
  • 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
    Great support
  • 1
    Massive data handling
  • 37
    Data Warehousing
  • 27
    Scalable
  • 16
    SQL
  • 14
    Backed by Amazon
  • 5
    Encryption
  • 1
    Cheap and reliable
  • 1
    Isolation
  • 1
    Best Cloud DW Performance
  • 1
    Fast columnar storage
  • 13
    Simple UI and autoscaling clusters
  • 9
    Feature to use AWS Spot pricing
  • 7
    Real-time data insights through Spark Notebook
  • 6
    Hyper elastic and scalable
  • 6
    Easy to manage costs
  • 6
    Easy to configure, deploy, and run Hadoop clusters
  • 6
    Optimized Spark, Hive, Presto, Hadoop 2, HBase clusters
  • 4
    Gracefully Scale up & down with zero human intervention
  • 4
    Backed by Amazon
  • 2
    All-in-one platform
  • 2
    Backed by Azure

Sign up to add or upvote prosMake informed product decisions

Sign up to add or upvote consMake informed product decisions

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.

What is 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.

What is Qubole?

Qubole is a cloud based service that makes big data easy for analysts and data engineers.

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

What companies use Amazon EMR?
What companies use Amazon Redshift?
What companies use Qubole?

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

What tools integrate with Amazon EMR?
What tools integrate with Amazon Redshift?
What tools integrate with Qubole?

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

Blog Posts

Aug 28 2019 at 3:10AM

Segment

+16
5
1990
Jul 9 2019 at 7:22PM

Blue Medora

+8
11
1710
+42
53
19582
+44
109
49827
What are some alternatives to Amazon EMR, Amazon Redshift, and Qubole?
Amazon EC2
It is a web service that provides resizable compute capacity in the cloud. It is designed to make web-scale computing easier for developers.
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 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.
Azure HDInsight
It is a cloud-based service from Microsoft for big data analytics that helps organizations process large amounts of streaming or historical data.
Databricks
Databricks Unified Analytics Platform, from the original creators of Apache Spark™, unifies data science and engineering across the Machine Learning lifecycle from data preparation to experimentation and deployment of ML applications.
See all alternatives
How developers use Amazon EMR, Amazon Redshift, and Qubole
Pinterest uses
Qubole

We ultimately migrated our Hadoop jobs to Qubole, a rising player in the Hadoop as a Service space. Given that EMR had become unstable at our scale, we had to quickly move to a provider that played well with AWS (specifically, spot instances) and S3. Qubole supported AWS/S3 and was relatively easy to get started on. After vetting Qubole and comparing its performance against alternatives (including managed clusters), we decided to go with Qubole

Olo uses
Amazon Redshift

Aggressive archiving of historical data to keep the production database as small as possible. Using our in-house soon-to-be-open-sourced ETL library, SharpShifter.

Andrew La Grange uses
Amazon EMR

We use Amazon EMR for all our Hadoop workloads.

Christian Moeller uses
Amazon Redshift

Connected to BI (Pentaho)

Kovid Rathee uses
Amazon Redshift

OLAP and BI