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.

Advice on Amazon Redshift, Cloudera Enterprise

datocrats-org

Jul 29, 2020

Needs adviceon

Amazon EC2

Tableau

PowerBI

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.

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Comments

Julien

CTO at Hawk

Sep 19, 2020

Decided

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

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Comments

Detailed Comparison

Amazon Redshift	Cloudera Enterprise
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.	Cloudera Enterprise includes CDH, the world’s most popular open source Hadoop-based platform, as well as advanced system management and data management tools plus dedicated support and community advocacy from our world-class team of Hadoop developers and experts.
Optimized for Data Warehousing- It uses columnar storage, data compression, and zone maps to reduce the amount of IO needed to perform queries. Redshift has a massively parallel processing (MPP) architecture, parallelizing and distributing SQL operations to take advantage of all available resources.;Scalable- With a few clicks of the AWS Management Console or a simple API call, you can easily scale the number of nodes in your data warehouse up or down as your performance or capacity needs change.;No Up-Front Costs- You pay only for the resources you provision. You can choose On-Demand pricing with no up-front costs or long-term commitments, or obtain significantly discounted rates with Reserved Instance pricing.;Fault Tolerant- Amazon Redshift has multiple features that enhance the reliability of your data warehouse cluster. All data written to a node in your cluster is automatically replicated to other nodes within the cluster and all data is continuously backed up to Amazon S3.;SQL - Amazon Redshift is a SQL data warehouse and uses industry standard ODBC and JDBC connections and Postgres drivers.;Isolation - Amazon Redshift enables you to configure firewall rules to control network access to your data warehouse cluster.;Encryption – With just a couple of parameter settings, you can set up Amazon Redshift to use SSL to secure data in transit and hardware-acccelerated AES-256 encryption for data at rest.<br>	Unified – one integrated system, bringing diverse users and application workloads to one pool of data on common infrastructure; no data movement required;Secure – perimeter security, authentication, granular authorization, and data protection;Governed – enterprise-grade data auditing, data lineage, and data discovery;Managed – native high-availability, fault-tolerance and self-healing storage, automated backup and disaster recovery, and advanced system and data management;Open – Apache-licensed open source to ensure your data and applications remain yours, and an open platform to connect with all of your existing investments in technology and skills
Statistics
Stacks 1.5K	Stacks 126
Followers 1.4K	Followers 172
Votes 108	Votes 5
Pros & Cons
Pros 41 Data Warehousing 27 Scalable 17 SQL 14 Backed by Amazon 5 Encryption	Pros 1 Easily management 1 Hybrid cloud 1 Multicloud 1 Scalability 1 Cheeper
Integrations
SQLite MySQL Oracle PL/SQL	No integrations available