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Amazon EMR vs Amazon Redshift vs Google BigQuery: What are the differences?
# Introduction
This Markdown provides a comparison between Amazon EMR, Amazon Redshift, and Google BigQuery in terms of key differences.
1. **Deployment Type**: Amazon EMR is a fully managed Hadoop framework to process large amounts of data, whereas Amazon Redshift is a fully managed data warehousing solution that is optimized for high-performance analysis at scale. Google BigQuery is a serverless, highly scalable, and cost-effective multi-cloud data warehouse for analytics.
2. **Data Processing Approach**: Amazon EMR processes data using Apache Hadoop and Spark, which allows for complex data transformations and analysis. Amazon Redshift integrates with existing Business Intelligence tools and SQL-based analytics. Google BigQuery supports a SQL-like query language and provides real-time analytics.
3. **Scalability**: Amazon EMR can scale both horizontally and vertically based on demand by adding or removing nodes. Amazon Redshift offers automatic scaling capabilities to handle growing workloads efficiently. Google BigQuery is designed to automatically scale to match the query workload in real-time.
4. **Pricing Structure**: Amazon EMR pricing is based on the type and number of EC2 instances used, along with additional charges for storage. Amazon Redshift pricing is based on the type and number of nodes in the cluster, as well as the amount of data stored. Google BigQuery pricing is based on the amount of data processed for queries and storage costs.
5. **Integration with Other Services**: Amazon EMR integrates seamlessly with other AWS services such as S3, DynamoDB, and Kinesis for data ingestion and storage. Amazon Redshift integrates with various AWS services for data loading and extraction. Google BigQuery integrates with Google Cloud services like Cloud Storage, Dataflow, and Dataprep for data analytics workflows.
In Summary, this Markdown highlights the key differences in deployment type, data processing approach, scalability, pricing structure, and integration capabilities between Amazon EMR, Amazon Redshift, and Google BigQuery.
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
Pros of Google BigQuery
- High Performance28
- Easy to use25
- Fully managed service22
- Cheap Pricing19
- Process hundreds of GB in seconds16
- Big Data12
- Full table scans in seconds, no indexes needed11
- Always on, no per-hour costs8
- Good combination with fluentd6
- Machine learning4
- Easy to manage1
- Easy to learn0
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Cons of Amazon EMR
Cons of Amazon Redshift
Cons of Google BigQuery
- You can't unit test changes in BQ data1
- Sdas0