Apache Spark vs Laravel Spark

Overview

Apache Spark

Stacks3.1K

Followers3.5K

Votes140

GitHub Stars42.2K

Forks28.9K

Laravel Spark

Stacks81

Followers144

Votes0

Apache Spark vs Laravel Spark: What are the differences?

Apache Spark and Laravel Spark are popular frameworks used in the field of big data processing and web development, respectively. Below are the key differences between Apache Spark and Laravel Spark:

Data Processing vs. Web Development: The main difference between Apache Spark and Laravel Spark lies in their primary use cases. Apache Spark is a distributed computing framework used primarily for processing large volumes of data in parallel, while Laravel Spark is a web application framework specifically designed for building web applications and APIs.
Technology Stack: Apache Spark is built on top of the Spark Core engine, which provides in-memory computing capabilities and supports various data processing tasks, whereas Laravel Spark is built on top of the Laravel PHP framework, which offers a comprehensive set of tools and libraries for web development.
Scalability: Apache Spark is known for its ability to scale horizontally across multiple nodes in a cluster to process large datasets efficiently, whereas Laravel Spark is more focused on providing a streamlined development experience for building web applications without the need for complex scalability requirements.
Language Support: Apache Spark primarily supports Scala, Java, and Python programming languages for writing data processing tasks, while Laravel Spark is specifically tailored for PHP developers who prefer using the Laravel framework for building web applications.
Community and Ecosystem: Apache Spark has a large and active open-source community with a wide range of third-party integrations and libraries available for data processing tasks, whereas Laravel Spark has a dedicated community of Laravel developers and a set of official packages and extensions for enhancing web application development within the Laravel ecosystem.
Complexity and Learning Curve: Apache Spark is known to have a steeper learning curve due to its distributed computing architecture and advanced data processing capabilities, whereas Laravel Spark offers a more beginner-friendly experience with its well-documented API and user-friendly features for web development tasks.

In Summary, Apache Spark is designed for data processing tasks in a distributed environment, while Laravel Spark is focused on simplifying web application development within the Laravel PHP framework.

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Advice on Apache Spark, Laravel Spark

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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Comments

Detailed Comparison

Apache Spark	Laravel 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.	Spark is a Laravel package that provides scaffolding for all of the stuff you don't want to code. Subscription billing? We got that. Invoices? No problem.
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	Subscriptions; Invoices; PayPal support; Per seat billing; Frontend freedom
Statistics
GitHub Stars 42.2K	GitHub Stars -
GitHub Forks 28.9K	GitHub Forks -
Stacks 3.1K	Stacks 81
Followers 3.5K	Followers 144
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	PHP Laravel

What are some alternatives to Apache Spark, Laravel Spark?

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 Laravel Spark: What are the differences?

Apache Spark and Laravel Spark are popular frameworks used in the field of big data processing and web development, respectively. Below are the key differences between Apache Spark and Laravel Spark:

Data Processing vs. Web Development: The main difference between Apache Spark and Laravel Spark lies in their primary use cases. Apache Spark is a distributed computing framework used primarily for processing large volumes of data in parallel, while Laravel Spark is a web application framework specifically designed for building web applications and APIs.
Technology Stack: Apache Spark is built on top of the Spark Core engine, which provides in-memory computing capabilities and supports various data processing tasks, whereas Laravel Spark is built on top of the Laravel PHP framework, which offers a comprehensive set of tools and libraries for web development.
Scalability: Apache Spark is known for its ability to scale horizontally across multiple nodes in a cluster to process large datasets efficiently, whereas Laravel Spark is more focused on providing a streamlined development experience for building web applications without the need for complex scalability requirements.
Language Support: Apache Spark primarily supports Scala, Java, and Python programming languages for writing data processing tasks, while Laravel Spark is specifically tailored for PHP developers who prefer using the Laravel framework for building web applications.
Community and Ecosystem: Apache Spark has a large and active open-source community with a wide range of third-party integrations and libraries available for data processing tasks, whereas Laravel Spark has a dedicated community of Laravel developers and a set of official packages and extensions for enhancing web application development within the Laravel ecosystem.
Complexity and Learning Curve: Apache Spark is known to have a steeper learning curve due to its distributed computing architecture and advanced data processing capabilities, whereas Laravel Spark offers a more beginner-friendly experience with its well-documented API and user-friendly features for web development tasks.

Apache Spark vs Laravel Spark

Overview