Data warehouses are data management systems that perform queries on information collected from different sources and run real-time analysis to make better strategies. These processes use a wide range of technology and machine learning applications to conduct analysis on large data sets and generate revenue.
Today, many online businesses and platforms with big data are looking for ways to reduce costs and improve customer experience. Facebook, messaging service providers, retailers are a few companies and applications that store big data. These platforms receive a large amount of traffic and data on a daily basis.
To deliver quality results and improved customer experience, businesses require solutions that can analyze these large data. One can find various cloud-based Data Warehousing solutions online, but we have listed the two popular ones – Amazon Redshift vs Cassandra.
Follow the two popular solutions that can help run queries on these large data volumes and generate revenue. Learn more about their features and select the one according to your project requirement.
What is Amazon Redshift?
AWS Redshift is a fully-managed data warehousing solution from Amazon Web Services that allows analysts to manage data sets on a large scale and query in seconds. It involves no upfront costs, setup, and maintenance.
Most businesses prefer Redshift over traditional data warehouse solutions due to its compliance features, quick performance, and scalable data processing solutions. Also, it offers various data analytics tools and machine learning applications.
You can analyze all your collected data with the help of various business intelligence tools available in the Redshift. It is a cloud-based suite of data management and analytics tools that helps businesses store all data and provide insights for better decision-making.
With AWS Redshift, companies have to no longer invest in time, money, and expertise as it offers a complete infrastructure to help optimize operations, maintain efficiency, and generate revenue.
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Why Amazon Redshift?
Amazon Redshift is a highly scalable and cost-effective data warehouse solution compared to other data warehouses. Also, it offers end-to-end data encryption and various other compliance and security features that make it a top choice. It is too fast and supports massively parallel processing (MPP) architecture. Further, users can reduce the data size by compressing it.
If you are using a data warehouse solution that is costing you a lot and queries are taking a long time to run, we recommend shifting to Amazon Redshift. It is cheaper, manages all hardware issues, and offers easy monitoring with its AWS Console. Also, one can set up alerts to get quick updates on noticing any potential issues.
Key Features of Amazon Redshift
Fault tolerance: Unlike other data warehouse solutions, Redshift ensures the continuous and smooth functioning of nodes at all times. If any single component fails or the clusters go offline, the data warehouse automatically creates copies of data to the other nodes. Thus, it offers continuous operations.
End-to-end data encryption: Encryption in Amazon Redshift is an optional setting but a great feature to protect your sensitive data. Using this highly customizable end-to-end data encryption feature, Redshift helps maintain the complete privacy of your data. Also, users have full access to configure and employ a customer or AWS-managed key to modify an unencrypted cluster.
Column-oriented databases: This feature enables organizations to store data into rows or columns for increased speed. With the help of column-oriented databases, the solution performs massive data processing jobs faster.
Massively parallel processing (MPP): This feature allows users to divide large data sets into small tasks and distribute them to various computer nodes for quick analysis. All the nodes perform computations at the same time and deliver fast query performance.
What is Cassandra?
Apache Cassandra is a secure, open-source distributed database management system designed to store and manage large volumes of data across various servers and data centers. It is a type of NoSQL database with peer-to-peer architecture and advanced features.
The popular distributed database is fault-tolerant, delivers zero downtime, offers linear scalability, and handles big data workloads with no single point of failure.
Why Cassandra?
Apache Cassandra is a highly reliable database system that provides real-time analysis. Also, it is a highly consistent and scalable, schema-free distributed database system that can manage large data sets across different data centers.
Today, many high-profile companies like eBay, Twitter, etc., choose Cassandra for its excellent features that ensure the performance never gets affected even if a particular node fails due to any technical issue.
Furthermore, the database nodes of Cassandra can read/write requests regardless of your location. Also, users can process queries at a faster speed compared to other alternate solutions.
Key Features of Cassandra
Highly Scalable: Users can easily add more hardware to the system. As a result, you can store more data and customers as per the requirement.
Security: It supports an audit logging feature that aids companies in monitoring all DML, DDL, and DCL activities and conducting real-time operational analysis without impacting workload performance. With proper analysis, users can easily track suspicious events or threats.
Quick Response Time: Users have full access to add more nodes to the cluster without worrying about the complexities. Thus, the throughput increases, which results in a quick response time.
Fault-Tolerant: In Cassandra, each node plays the same role and carries similar information. Thus, if any node fails, the other node will replace it and continue performing tasks. Also, Cassandra is highly scalable, which means extra nodes can be added to the system. Thus, the database system ensures high performance with no single point of failure. Learn more about the Cassandra Data Models.
Redshift vs Cassandra: Key differences
Both Redshift and Cassandra are used to manage large volumes of data and are fault-tolerant, but have certain differences that make them different from one another. Here are a few comparisons of Redshift vs Cassandra:
Redshift vs Cassandra: Database Model
AWS Redshift is a data warehouse solution that uses business intelligence tools to store and manage large data sets. Apache Cassandra is a distributed database system that store large data volumes across different data centers.
Redshift vs Cassandra: Description
Most businesses opt for Redshift or Cassandra when it comes to storing or analyzing big data. Amazon Redshift is a large-scale data warehouse service that can be used with business intelligence tools, while Cassandra is a wide-column store based on ideas of BigTable and DynamoDB info.
Redshift vs Cassandra: Architecture
Redshift has a massively parallel processing (MPP) architecture, while Cassandra has a peer-to-peer architecture. In the Massive Parallel Processing architecture, developers have full access to add more compute resources as per the project requirement. Redshift follows the “divide and distribute” approach, which enables the fast execution of complex queries in Redshift.
In the peer-to-peer architecture, each node comprises similar capabilities and responsibilities. Thus, when a node fails, the other replaces it and serves the purpose.
Cassandra was designed for fast writing and read records based on keys, whereas, Redshift was designed for fast aggregations (MPP). Redshift comprises various features, including Query optimizer, Data compression, Result caching, etc that result in fast execution of queries.
Redshift vs Cassandra: License
- Cassandra is a NoSQL distributed database known for its scalability and high availability. It has an open-source license, while Redshift is a data warehousing solution known for scalable data processing solutions and fast performance. Redshift supports a commercial license.
Redshift vs Cassandra: Operating System Support
- Redshift has a hosted server operating system, and Cassandra supports BSD, Linux, OS X, Windows, etc.
- Redshift vs Cassandra: Language Compatibility
- Redshift is compatible with all languages compatible with JDBC/ODBC. On the other hand, Cassandra supports Javascript, C++, C#, Go, PHP, Python, etc.
Redshift vs Cassandra: Foreign Key
A foreign key is a single column or group of columns in a relational database table that must match the values of the primary key of another table. Thus, establishing a link between them. Redshift supports foreign keys to create a few more efficient query plans, while Cassandra supports no such keys.
Redshift vs Cassandra: Company Who Uses
Starbucks, Facebook, Rackspace, etc., are a few high-profile companies that prefer Cassandra, whereas Nubank, Bitpanda, Coursera, Lyft, etc., use Amazon Redshift.
Redshift vs Cassandra: Use Cases
Amazon Redshift is used by companies that prefer business intelligence tools to build powerful reports and deploy applications faster. It is a cost-effective solution that helps run queries on semi-structured and structured data.
Most Mobile phone companies, messaging service providers, and retailers use Cassandra as they store data at a large scale. Also, the applications that receive data at high speed prefer Cassandra. It is also a great option for social media providers and cloud-based companies that manage large data sets for analysis and recommendations.
Conclusion
Data Warehouse Solutions have become important for cloud-based businesses with big data.
AWS Redshift belongs to the “Big Data as a Service” category, whereas Cassandra is distributed database. Both the solutions are fault-tolerant, highly scalable, and are used to handle large data sets. They help run faster queries and perform analysis for better decision-making and retaining customers.
Go through some of the above-listed key differences of Amazon Redshift vs Cassandra to select the one suitable for your project requirement.
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Veeresh is a skilled professional specializing in JDBC, REST API, Linux, and Shell Scripting. With a knack for resolving complex issues and implementing Python transformations, he plays a crucial role in enhancing Hevo's data integration solutions.
