Apache Spark vs Kudu

Overview

Apache Spark

Stacks3.1K

Followers3.5K

Votes140

GitHub Stars42.2K

Forks28.9K

Apache Kudu

Stacks71

Followers259

Votes10

GitHub Stars828

Forks282

Apache Spark vs Kudu: What are the differences?

Introduction: Apache Spark and Kudu are both big data technologies used for processing and storing large amounts of data. However, they have some key differences in terms of their architecture and use cases.

Data Storage: Apache Spark is primarily an in-memory data processing engine that is used for batch and stream processing. It does not have built-in storage capabilities and relies on external storage systems like Hadoop Distributed File System (HDFS) or cloud storage. On the other hand, Kudu is a columnar storage engine that is designed for fast analytics on rapidly changing data. It provides a high-throughput, low-latency storage solution for structured data.
Data Model: Apache Spark is built around the concept of Resilient Distributed Datasets (RDDs) and DataFrames/Datasets. RDDs are fault-tolerant, immutable collections of records that can be operated on in parallel. DataFrames/Datasets are higher-level APIs that provide a more structured and efficient way of working with data. Kudu, on the other hand, stores data in tables with rows and columns, similar to traditional relational databases. It provides ACID-compliant transactions and supports complex queries and aggregations.
Use Cases: Apache Spark is well-suited for a wide range of data processing tasks, including ETL (extract, transform, load), machine learning, graph processing, and real-time analytics. It is highly scalable and can run on a cluster of machines. Kudu, on the other hand, is optimized for fast analytical queries on large volumes of data. It is ideal for applications that require low-latency access to data, such as real-time reporting, online analytical processing (OLAP), and time-series analysis.
Fault Tolerance: Apache Spark provides fault tolerance through lineage information and resilient distributed datasets (RDDs). If a partition of an RDD is lost, Spark can recompute it using the lineage information. Kudu, on the other hand, provides fault tolerance through data replication and distribution. Data stored in Kudu can be replicated across multiple nodes in a cluster to ensure high availability and reliability.
Consistency: Apache Spark offers eventual consistency, which means that updates to data may not be immediately reflected in all nodes of the cluster. This can lead to inconsistencies in the data while processing. On the contrary, Kudu provides strong consistency, ensuring that all reads and writes are immediately consistent across all nodes. This makes Kudu suitable for transactional workloads where data consistency is critical.
Data Processing Paradigm: Apache Spark follows a batch and stream processing paradigm and supports various processing engines like batch, streaming, SQL, machine learning, and graph processing. In contrast, Kudu is primarily focused on fast analytical queries and is more suitable for OLAP workloads that involve complex queries and aggregations on structured data.

In Summary, Apache Spark and Kudu differ in terms of data storage, data model, use cases, fault tolerance, consistency, and data processing paradigm, making them suitable for different types of big data processing tasks.

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Advice on Apache Spark, Apache Kudu

Nilesh

Technical Architect at Self Employed

Jul 8, 2020

Needs adviceon

Elasticsearch

Kafka

We have a Kafka topic having events of type A and type B. We need to perform an inner join on both type of events using some common field (primary-key). The joined events to be inserted in Elasticsearch.

In usual cases, type A and type B events (with same key) observed to be close upto 15 minutes. But in some cases they may be far from each other, lets say 6 hours. Sometimes event of either of the types never come.

In all cases, we should be able to find joined events instantly after they are joined and not-joined events within 15 minutes.

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Detailed Comparison

Apache Spark	Apache Kudu
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.	A new addition to the open source Apache Hadoop ecosystem, Kudu completes Hadoop's storage layer to enable fast analytics on fast data.
Run programs up to 100x faster than Hadoop MapReduce in memory, or 10x faster on disk;Write applications quickly in Java, Scala or Python;Combine SQL, streaming, and complex analytics;Spark runs on Hadoop, Mesos, standalone, or in the cloud. It can access diverse data sources including HDFS, Cassandra, HBase, S3	-
Statistics
GitHub Stars 42.2K	GitHub Stars 828
GitHub Forks 28.9K	GitHub Forks 282
Stacks 3.1K	Stacks 71
Followers 3.5K	Followers 259
Votes 140	Votes 10
Pros & Cons
Pros 61 Open-source 48 Fast and Flexible 8 One platform for every big data problem 8 Great for distributed SQL like applications 6 Easy to install and to use Cons 4 Speed	Pros 10 Realtime Analytics Cons 1 Restart time
Integrations
No integrations available	Hadoop

What are some alternatives to Apache Spark, Apache Kudu?

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.

Apache Kylin

Apache Kylin™ is an open source Distributed Analytics Engine designed to provide SQL interface and multi-dimensional analysis (OLAP) on Hadoop/Spark supporting extremely large datasets, originally contributed from eBay Inc.

Splunk

It provides the leading platform for Operational Intelligence. Customers use it to search, monitor, analyze and visualize machine data.

Apache Impala

Impala is a modern, open source, MPP SQL query engine for Apache Hadoop. Impala is shipped by Cloudera, MapR, and Amazon. With Impala, you can query data, whether stored in HDFS or Apache HBase – including SELECT, JOIN, and aggregate functions – in real time.

Vertica

It provides a best-in-class, unified analytics platform that will forever be independent from underlying infrastructure.

Azure Synapse

It is an analytics service that brings together enterprise data warehousing and Big Data analytics. It gives you the freedom to query data on your terms, using either serverless on-demand or provisioned resources—at scale. It brings these two worlds together with a unified experience to ingest, prepare, manage, and serve data for immediate BI and machine learning needs.

Related Comparisons

Apache Spark vs Kudu: What are the differences?

Data Storage: Apache Spark is primarily an in-memory data processing engine that is used for batch and stream processing. It does not have built-in storage capabilities and relies on external storage systems like Hadoop Distributed File System (HDFS) or cloud storage. On the other hand, Kudu is a columnar storage engine that is designed for fast analytics on rapidly changing data. It provides a high-throughput, low-latency storage solution for structured data.
Data Model: Apache Spark is built around the concept of Resilient Distributed Datasets (RDDs) and DataFrames/Datasets. RDDs are fault-tolerant, immutable collections of records that can be operated on in parallel. DataFrames/Datasets are higher-level APIs that provide a more structured and efficient way of working with data. Kudu, on the other hand, stores data in tables with rows and columns, similar to traditional relational databases. It provides ACID-compliant transactions and supports complex queries and aggregations.
Use Cases: Apache Spark is well-suited for a wide range of data processing tasks, including ETL (extract, transform, load), machine learning, graph processing, and real-time analytics. It is highly scalable and can run on a cluster of machines. Kudu, on the other hand, is optimized for fast analytical queries on large volumes of data. It is ideal for applications that require low-latency access to data, such as real-time reporting, online analytical processing (OLAP), and time-series analysis.
Fault Tolerance: Apache Spark provides fault tolerance through lineage information and resilient distributed datasets (RDDs). If a partition of an RDD is lost, Spark can recompute it using the lineage information. Kudu, on the other hand, provides fault tolerance through data replication and distribution. Data stored in Kudu can be replicated across multiple nodes in a cluster to ensure high availability and reliability.
Consistency: Apache Spark offers eventual consistency, which means that updates to data may not be immediately reflected in all nodes of the cluster. This can lead to inconsistencies in the data while processing. On the contrary, Kudu provides strong consistency, ensuring that all reads and writes are immediately consistent across all nodes. This makes Kudu suitable for transactional workloads where data consistency is critical.
Data Processing Paradigm: Apache Spark follows a batch and stream processing paradigm and supports various processing engines like batch, streaming, SQL, machine learning, and graph processing. In contrast, Kudu is primarily focused on fast analytical queries and is more suitable for OLAP workloads that involve complex queries and aggregations on structured data.

Apache Spark vs Kudu

Overview