Apache Kudu vs Azure Cosmos DB

Overview

Azure Cosmos DB

Stacks594

Followers1.1K

Votes130

Apache Kudu

Stacks71

Followers259

Votes10

GitHub Stars828

Forks282

Apache Kudu vs Azure Cosmos DB: What are the differences?

Data model: Apache Kudu is a columnar storage engine, optimized for analytics workloads with real-time insert/update/delete capabilities. On the other hand, Azure Cosmos DB is a NoSQL database service that offers a globally distributed database with multiple data models like document, key-value, graph, and wide-column.
Consistency Model: Apache Kudu provides strong consistency, ensuring that all reads reflect the most recent write. In contrast, Azure Cosmos DB offers five consistency levels to choose from, including strong, bounded staleness, session, consistent prefix, and eventual consistency.
Workload support: Apache Kudu is well-suited for OLAP (Online Analytical Processing) workloads, where analytical queries demand fast reads and writes on a large amount of data. Azure Cosmos DB, on the other hand, is designed for globally distributed operational workloads, offering low-latency reads and writes across the globe.
Query language: Apache Kudu integrates well with Apache Spark and Impala, utilizing SQL-like query languages for data analysis. Azure Cosmos DB supports SQL, MongoDB, Cassandra, Gremlin, and Table APIs, providing users with diverse query language options depending on their preferred data model.
Storage optimization: Apache Kudu utilizes a combination of in-memory and disk-based storage for optimal performance, with data stored in columnar format for efficient query processing. In contrast, Azure Cosmos DB offers automatic indexing and scaling, allowing users to adjust storage capacity and throughput based on their workload requirements.

In Summary, Apache Kudu and Azure Cosmos DB differ in terms of their data model, consistency model, workload support, query language options, and storage optimization features.

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Detailed Comparison

Azure Cosmos DB	Apache Kudu
Azure DocumentDB is a fully managed NoSQL database service built for fast and predictable performance, high availability, elastic scaling, global distribution, and ease of development.	A new addition to the open source Apache Hadoop ecosystem, Kudu completes Hadoop's storage layer to enable fast analytics on fast data.
Fully managed with 99.99% Availability SLA;Elastically and highly scalable (both throughput and storage);Predictable low latency: <10ms @ P99 reads and <15ms @ P99 fully-indexed writes;Globally distributed with multi-region replication;Rich SQL queries over schema-agnostic automatic indexing;JavaScript language integrated multi-record ACID transactions with snapshot isolation;Well-defined tunable consistency models: Strong, Bounded Staleness, Session, and Eventual	-
Statistics
GitHub Stars -	GitHub Stars 828
GitHub Forks -	GitHub Forks 282
Stacks 594	Stacks 71
Followers 1.1K	Followers 259
Votes 130	Votes 10
Pros & Cons
Pros 28 Best-of-breed NoSQL features 22 High scalability 15 Globally distributed 14 Automatic indexing over flexible json data model 10 Tunable consistency Cons 18 Pricing 4 Poor No SQL query support	Pros 10 Realtime Analytics Cons 1 Restart time
Integrations
Azure Machine Learning MongoDB Hadoop Java Azure Functions Azure Container Service Azure Storage Azure Websites Apache Spark Python	Hadoop

What are some alternatives to Azure Cosmos DB, Apache Kudu?

Amazon DynamoDB

With it , you can offload the administrative burden of operating and scaling a highly available distributed database cluster, while paying a low price for only what you use.

Apache Spark

Spark is a fast and general processing engine compatible with Hadoop data. It can run in Hadoop clusters through YARN or Spark's standalone mode, and it can process data in HDFS, HBase, Cassandra, Hive, and any Hadoop InputFormat. It is designed to perform both batch processing (similar to MapReduce) and new workloads like streaming, interactive queries, and machine learning.

Cloud Firestore

Cloud Firestore is a NoSQL document database that lets you easily store, sync, and query data for your mobile and web apps - at global scale.

Presto

Distributed SQL Query Engine for Big Data

Amazon Athena

Amazon Athena is an interactive query service that makes it easy to analyze data in Amazon S3 using standard SQL. Athena is serverless, so there is no infrastructure to manage, and you pay only for the queries that you run.

Apache Flink

Apache Flink is an open source system for fast and versatile data analytics in clusters. Flink supports batch and streaming analytics, in one system. Analytical programs can be written in concise and elegant APIs in Java and Scala.

lakeFS

It is an open-source data version control system for data lakes. It provides a “Git for data” platform enabling you to implement best practices from software engineering on your data lake, including branching and merging, CI/CD, and production-like dev/test environments.

Druid

Druid is a distributed, column-oriented, real-time analytics data store that is commonly used to power exploratory dashboards in multi-tenant environments. Druid excels as a data warehousing solution for fast aggregate queries on petabyte sized data sets. Druid supports a variety of flexible filters, exact calculations, approximate algorithms, and other useful calculations.

Cloudant

Cloudant’s distributed database as a service (DBaaS) allows developers of fast-growing web and mobile apps to focus on building and improving their products, instead of worrying about scaling and managing databases on their own.

Google Cloud Bigtable

Google Cloud Bigtable offers you a fast, fully managed, massively scalable NoSQL database service that's ideal for web, mobile, and Internet of Things applications requiring terabytes to petabytes of data. Unlike comparable market offerings, Cloud Bigtable doesn't require you to sacrifice speed, scale, or cost efficiency when your applications grow. Cloud Bigtable has been battle-tested at Google for more than 10 years—it's the database driving major applications such as Google Analytics and Gmail.

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Apache Kudu vs Azure Cosmos DB: What are the differences?

Data model: Apache Kudu is a columnar storage engine, optimized for analytics workloads with real-time insert/update/delete capabilities. On the other hand, Azure Cosmos DB is a NoSQL database service that offers a globally distributed database with multiple data models like document, key-value, graph, and wide-column.
Consistency Model: Apache Kudu provides strong consistency, ensuring that all reads reflect the most recent write. In contrast, Azure Cosmos DB offers five consistency levels to choose from, including strong, bounded staleness, session, consistent prefix, and eventual consistency.
Workload support: Apache Kudu is well-suited for OLAP (Online Analytical Processing) workloads, where analytical queries demand fast reads and writes on a large amount of data. Azure Cosmos DB, on the other hand, is designed for globally distributed operational workloads, offering low-latency reads and writes across the globe.
Query language: Apache Kudu integrates well with Apache Spark and Impala, utilizing SQL-like query languages for data analysis. Azure Cosmos DB supports SQL, MongoDB, Cassandra, Gremlin, and Table APIs, providing users with diverse query language options depending on their preferred data model.
Storage optimization: Apache Kudu utilizes a combination of in-memory and disk-based storage for optimal performance, with data stored in columnar format for efficient query processing. In contrast, Azure Cosmos DB offers automatic indexing and scaling, allowing users to adjust storage capacity and throughput based on their workload requirements.

In Summary, Apache Kudu and Azure Cosmos DB differ in terms of their data model, consistency model, workload support, query language options, and storage optimization features.