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Amazon Redshift vs Kudu: What are the differences?
Introduction
Amazon Redshift and Kudu are both data storage and processing platforms used in big data analytics. While they share similarities in some aspects, there are significant differences that set them apart. In this comparison, we will highlight six key differences between Amazon Redshift and Kudu.
Data Model: Amazon Redshift follows a columnar data model where data is stored and processed in columns rather than rows. This ensures efficient querying and analysis of large datasets. On the other hand, Kudu adopts a row-based data model, suited for real-time analytics and efficient random access to individual records.
Data Updateability: In Amazon Redshift, data is primarily loaded through bulk load operations, making updates to existing data challenging. Redshift is designed for batch data processing rather than real-time or frequent updates. In contrast, Kudu supports highly optimized random write operations, allowing quick updates, deletions, and inserts without compromising performance.
Consistency and Durability: Amazon Redshift provides eventual consistency, meaning changes to the database are not immediately propagated to all instances. While this approach enhances performance, it may result in temporary inconsistencies for a short period. Kudu, however, offers strong consistency and durability guarantees, ensuring all updates are immediately visible to all readers.
Data Tuning: Amazon Redshift relies on query planner and optimizer to optimize queries for performance, where users need to manually define and tune sort keys, distribution styles, and compression encodings. In contrast, Kudu has an automatic data tuning feature that analyzes the data and applies optimizations, such as intelligent partitioning and automatic indexing, to improve query performance without user intervention.
Integration with Ecosystem: Amazon Redshift seamlessly integrates with various AWS services like S3, Glue, and Athena, allowing users to build a comprehensive big data ecosystem. Kudu, on the other hand, integrates well with Apache Hadoop ecosystem components like Apache Impala and Apache Spark, strengthening its position within the Hadoop ecosystem.
Data Replication: Amazon Redshift relies on replication to other Amazon Redshift clusters in different regions for disaster recovery and high availability. It does not provide automatic data replication to non-Redshift clusters. On the contrary, Kudu supports automatic cross-cluster data replication, making it easier to create highly available and fault-tolerant solutions within a multi-cluster environment.
In summary, Amazon Redshift and Kudu differ in their data models, updateability, consistency, tuning mechanisms, ecosystem integration, and data replication capabilities. Understanding these differences is crucial in selecting the appropriate platform based on specific use cases and requirements.
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 Apache Kudu
- Realtime Analytics10
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Cons of Amazon Redshift
Cons of Apache Kudu
- Restart time1