Apache Spark vs Kapacitor

Overview

Apache Spark

Stacks3.1K

Followers3.5K

Votes140

GitHub Stars42.2K

Forks28.9K

Kapacitor

Stacks40

Followers54

Votes0

Apache Spark vs Kapacitor: What are the differences?

Apache Spark and Kapacitor are both powerful tools used for data processing and analysis. However, they have some distinct differences that set them apart from each other.

Nature: Apache Spark is a distributed computing system that is primarily used for big data processing and analytics, while Kapacitor is a real-time streaming data processing engine specifically designed to work with time series data.
Use Case: Apache Spark is commonly used for batch processing and iterative algorithms on large datasets, whereas Kapacitor is ideal for handling real-time streaming data and implementing time-based alerting and anomaly detection.
Architecture: Apache Spark follows a master-slave architecture with a central coordinator (Spark Master) that allocates resources and schedules tasks on worker nodes (Spark Workers). In contrast, Kapacitor uses a more streamlined pipeline architecture where the data flows sequentially through various stages of processing.
Data Processing Model: Apache Spark uses an in-memory processing model for faster data processing, making it suitable for complex analytics and machine learning tasks. Kapacitor, on the other hand, focuses on processing data as it streams in real-time, enabling quick responses to changing data patterns.
Scalability: Apache Spark is known for its scalability and can efficiently handle large datasets by distributing processing tasks across a cluster of machines. While Kapacitor can also scale horizontally, it is more optimized for handling high-velocity data streams with low latency requirements.
Integration with Other Systems: Apache Spark provides extensive integration with various data sources and libraries in the Hadoop ecosystem, making it a versatile tool for building data pipelines. In comparison, Kapacitor is tightly integrated with InfluxDB, a time series database, and is well-suited for monitoring and analyzing time-based metrics.

In Summary, Apache Spark and Kapacitor have distinct differences in their nature, use case, architecture, data processing model, scalability, and integration with other systems.

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Advice on Apache Spark, Kapacitor

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	Kapacitor
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.	It is a native data processing engine for InfluxDB 1.x and is an integrated component in the InfluxDB 2.0 platform. It can process both stream and batch data from InfluxDB, acting on this data in real-time via its programming language TICKscript.
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	can process both stream and batch data ; acting on data in real-time
Statistics
GitHub Stars 42.2K	GitHub Stars -
GitHub Forks 28.9K	GitHub Forks -
Stacks 3.1K	Stacks 40
Followers 3.5K	Followers 54
Votes 140	Votes 0
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	No community feedback yet
Integrations
No integrations available	InfluxDB Kafka

What are some alternatives to Apache Spark, Kapacitor?

Presto

Distributed SQL Query Engine for Big Data

Apache NiFi

An easy to use, powerful, and reliable system to process and distribute data. It supports powerful and scalable directed graphs of data routing, transformation, and system mediation logic.

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 Storm

Apache Storm is a free and open source distributed realtime computation system. Storm makes it easy to reliably process unbounded streams of data, doing for realtime processing what Hadoop did for batch processing. Storm has many use cases: realtime analytics, online machine learning, continuous computation, distributed RPC, ETL, and more. Storm is fast: a benchmark clocked it at over a million tuples processed per second per node. It is scalable, fault-tolerant, guarantees your data will be processed, and is easy to set up and operate.

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.

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Apache Spark vs Kapacitor: What are the differences?

Apache Spark and Kapacitor are both powerful tools used for data processing and analysis. However, they have some distinct differences that set them apart from each other.

Nature: Apache Spark is a distributed computing system that is primarily used for big data processing and analytics, while Kapacitor is a real-time streaming data processing engine specifically designed to work with time series data.
Use Case: Apache Spark is commonly used for batch processing and iterative algorithms on large datasets, whereas Kapacitor is ideal for handling real-time streaming data and implementing time-based alerting and anomaly detection.
Architecture: Apache Spark follows a master-slave architecture with a central coordinator (Spark Master) that allocates resources and schedules tasks on worker nodes (Spark Workers). In contrast, Kapacitor uses a more streamlined pipeline architecture where the data flows sequentially through various stages of processing.
Data Processing Model: Apache Spark uses an in-memory processing model for faster data processing, making it suitable for complex analytics and machine learning tasks. Kapacitor, on the other hand, focuses on processing data as it streams in real-time, enabling quick responses to changing data patterns.
Scalability: Apache Spark is known for its scalability and can efficiently handle large datasets by distributing processing tasks across a cluster of machines. While Kapacitor can also scale horizontally, it is more optimized for handling high-velocity data streams with low latency requirements.
Integration with Other Systems: Apache Spark provides extensive integration with various data sources and libraries in the Hadoop ecosystem, making it a versatile tool for building data pipelines. In comparison, Kapacitor is tightly integrated with InfluxDB, a time series database, and is well-suited for monitoring and analyzing time-based metrics.

In Summary, Apache Spark and Kapacitor have distinct differences in their nature, use case, architecture, data processing model, scalability, and integration with other systems.