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Amazon EMR vs Amazon Redshift: What are the differences?
Amazon EMR (Elastic MapReduce) and Amazon Redshift are both services offered by Amazon Web Services (AWS) for big data processing and analysis. While they may serve similar purposes, there are several key differences between the two services that make them suitable for different use cases.
Storage and Processing: Amazon EMR is designed for distributed processing of large datasets using frameworks like Apache Hadoop and Apache Spark. It allows users to run custom applications and process data in parallel across a cluster of EC2 instances. On the other hand, Amazon Redshift is a fully-managed data warehousing service that is optimized for online analytical processing (OLAP). It is ideal for running complex analytic queries on large datasets stored in a columnar format.
Data Structure and Query Performance: Amazon EMR can handle both structured and unstructured data, and provides flexibility in schema design. It excels at handling iterative data analysis and machine learning tasks. In contrast, Amazon Redshift is designed for structured data and supports SQL-based queries. It uses massively parallel processing to deliver fast query performance on large datasets, making it suitable for reporting and business intelligence scenarios.
Data Volume and Scalability: Amazon EMR can handle petabytes of data and scales horizontally by adding or removing compute nodes as needed. It provides an elastic and cost-effective solution for processing large volumes of data. On the other hand, Amazon Redshift is optimized for large-scale datasets and can handle terabytes to petabytes of data. It automatically distributes and parallelizes data across nodes for high performance, making it suitable for data warehousing scenarios.
Data Transfer and Integration: Amazon EMR supports various data sources and integrates well with other AWS services like Amazon S3 and Amazon DynamoDB. It provides tools for data import/export and enables seamless integration with other AWS services. Amazon Redshift also supports data import from various sources and can integrate with different data sources through JDBC/ODBC drivers. Additionally, it provides native integration with AWS Glue for data cataloging and integration.
Cost Structure: Amazon EMR offers a flexible pricing model based on EC2 instance usage, storage, and data transfer. It provides cost optimization options like spot instances for cost-effective processing. Amazon Redshift, on the other hand, has a separate cost structure based on compute node hours and the amount of data stored. It offers options for resize and pause, allowing users to scale up or down based on usage requirements.
Data Availability and Durability: Amazon EMR provides data durability by storing data on Amazon S3, which offers 99.999999999% durability. It also supports data replication for fault tolerance. Amazon Redshift provides high availability and fault tolerance by replicating data within the cluster and across multiple availability zones. It also offers automated backups and snapshots for data recovery.
In summary, Amazon EMR is designed for distributed processing of large datasets using frameworks like Hadoop and Spark, while Amazon Redshift is a fully-managed data warehousing service optimized for OLAP queries on large structured datasets. EMR provides flexibility, scalability, and cost-effectiveness for big data processing, while Redshift offers fast query performance, integration with various data sources, and high availability for data warehousing.
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.
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.
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.
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.
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
Pros of Amazon EMR
- On demand processing power15
- Don't need to maintain Hadoop Cluster yourself12
- Hadoop Tools7
- Elastic6
- Backed by Amazon4
- Flexible3
- Economic - pay as you go, easy to use CLI and SDKs3
- Don't need a dedicated Ops group2
- Massive data handling1
- Great support1
Pros of Amazon Redshift
- Data Warehousing41
- Scalable27
- SQL17
- Backed by Amazon14
- Encryption5
- Cheap and reliable1
- Isolation1
- Best Cloud DW Performance1
- Fast columnar storage1
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