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Amazon Redshift vs Cloudera Enterprise: What are the differences?
Introduction
This Markdown code provides a comparison of key differences between Amazon Redshift and Cloudera Enterprise.
Scalability: Amazon Redshift is a fully managed, petabyte-scale data warehouse service in the cloud. It offers high scalability by automatically provisioning and scaling the infrastructure based on workload demands. In contrast, Cloudera Enterprise is an on-premises solution that allows users to build and manage big data infrastructure using tools like Apache Hadoop and Apache Spark. While it can also scale, it requires manual provisioning and management of hardware resources.
Ease of Use: Amazon Redshift offers a user-friendly interface and simplified management, making it easier for developers and data analysts to quickly set up and query data. It provides a powerful, fully managed SQL engine with support for various business intelligence tools. On the other hand, Cloudera Enterprise requires more technical expertise and configuration to set up and maintain the big data infrastructure. It provides a comprehensive ecosystem but may require additional development efforts for data analysis and visualization.
Cost: Amazon Redshift offers a pay-per-use pricing model, where users are charged based on the amount of data stored and the data transferred. This makes it cost-effective for small to medium-sized businesses to start and scale their data warehousing needs. Cloudera Enterprise, being an on-premises solution, incurs higher upfront costs for hardware and infrastructure setup. However, it may be more cost-effective for large enterprises with existing data centers and significant data processing requirements.
Security: Amazon Redshift offers built-in security features, such as data encryption at rest and in transit, fine-grained access controls, and integration with AWS Identity and Access Management (IAM). It also supports audit logging and compliance with industry standards. Cloudera Enterprise provides security features like Kerberos authentication and authorization, data encryption, and role-based access controls. However, it requires additional configuration and setup compared to the out-of-the-box security features provided by Amazon Redshift.
Integration and Ecosystem: Amazon Redshift seamlessly integrates with other AWS services, such as AWS Glue, Amazon S3, and Amazon Machine Learning. This allows users to easily load data, perform transformations, and build machine learning models within the AWS environment. Cloudera Enterprise provides a comprehensive ecosystem of open-source tools like Hadoop, Spark, and Impala, which enable complex data processing and analytics. It also offers integrations with various third-party tools, allowing users to leverage their preferred technologies.
Data Storage and Processing: Amazon Redshift uses a columnar storage model, which enables high performance for analytical queries by reducing I/O and improving compression. It supports advanced compression techniques like zone maps and columnar encodings. Cloudera Enterprise uses a distributed file system like Hadoop Distributed File System (HDFS), which allows for scalable storage and processing of large datasets. It supports parallel processing frameworks like Spark, enabling real-time and batch data processing.
In summary, Amazon Redshift provides a scalable, easy-to-use, cost-effective, and secure cloud-based data warehousing solution with seamless integrations, while Cloudera Enterprise offers an on-premises big data infrastructure solution with a comprehensive ecosystem of open-source tools and distributed file systems.
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 Redshift
- Data Warehousing41
- Scalable27
- SQL17
- Backed by Amazon14
- Encryption5
- Cheap and reliable1
- Isolation1
- Best Cloud DW Performance1
- Fast columnar storage1
Pros of Cloudera Enterprise
- Scalability1
- Multicloud1
- Hybrid cloud1
- Easily management1
- Cheeper1
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