Top 10 Alternatives To Apache Spark in 2024
Apache Spark is an open-source unified analytics engine for large-scale data processing. With more than 28k GitHub stars, this analytics engine can be said as one of the most active open-sourced big data projects and is popular for its various intuitive features. Some of its features include ease of writing applications quickly in various languages, such as Java, Scala, Python, R, and SQL and accessibility in diverse data sources. Below here is a compilation of the top eight alternatives to Apache Spark. 1. Apache Hadoop Apache Hadoop is a framework that allows distributed processing of large data sets across clusters of computers using simple programming models. The framework is designed to scale up from single servers to thousands of machines, each offering local computation and storage. Apache Hadoop has its own file distribution system known as the HDFS (Hadoop Distributed File System). The file storing system is typically used for organising the files. 2. Google BigQuery Google BigQuery is one of the cloud-based big data analytics web services for processing very large read-only data sets. It is Google Cloud’s fully managed, petabyte-scale and cost-effective analytics data warehouse that lets developers run analytics over vast amounts of data in near real-time. 3. Apache Storm Apache Storm is an open-source distributed real-time computation system. Developers use this system mainly to process streams of data in real-time. Apache Storm has many use cases, including real-time analytics, online machine learning, continuous computation, distributed RPC, ETL, and more. Storm integrates with the database technologies; and its features include scalability, fault-tolerance as well as guarantees that the data will be processed in an easy manner and is simple to set up and operate. 4. Apache Flink Apache Flink is a framework, and a distributed processing engine meant for stateful computations over unbounded and bounded data streams. The framework has been created to run in all the common cluster environments and then perform computations at the in-memory speed at any scale. Flink can be used to develop and run many different types of applications due to its extensive features set. Some of its key features include support for stream and batch processing, sophisticated state management, event-time processing semantics, and exactly-once consistency guarantees for the state. 5. Lumify Lumify is a popular big data fusion, analysis, and visualisation platform that supports the development of actionable intelligence. This big data tool enables users to discover complex connections and explore diverse relationships in their data through a suite of analytic options, including graph visualisations, full-text faceted search, dynamic histograms, interactive geospatial views, and collaborative workspaces shared in real-time. It is a tool that empowers intelligence analysts to make the quick, informed decisions that our national security demands. 6. Apache Sqoop Apache Sqoop is a tool designed for efficiently transferring bulk data between Apache Hadoop and structured datastores such as relational databases. Basically, it is a tool that is designed to transfer data between Hadoop and relational databases or mainframes. Developers can use Sqoop to import data from a relational database management system such as MySQL or Oracle or a mainframe into the Hadoop Distributed File System (HDFS), transform the data in Hadoop MapReduce, and then export the data back into an RDBMS. 7. Elasticsearch Released in 2010, Elasticsearch is a popular, distributed, open-source search and analytics engine for all types of data, including textual, numerical, geospatial, structured, and unstructured. It is built on Apache Lucene and known for its simple REST APIs, distributed nature, speed, and scalability. The speed and scalability of Elasticsearch can be used for infrastructure metrics and container monitoring, application performance monitoring, geospatial data analysis and visualisation and more. 8. Presto Presto is an open-source distributed SQL query engine for running interactive analytic queries against data sources of all sizes. The engine was designed and written from the ground up for interactive analytics and approaches the speed of commercial data warehouses while scaling to organisations like Facebook. Facebook uses Presto for interactive queries against several internal data stores, including their 300PB data warehouse. 9. Snowflake Snowflake is a better alternative to Apache Spark for certain use cases due to its fully managed cloud-based platform. It automatically scales compute resources as needed, simplifying performance management and making it easier to handle varying workloads. Snowflake offers a pay-per-use model, charging only for the resources you use, which can result in cost savings, especially for variable workloads. Apache Spark can be more expensive due to its always-on infrastructure needs. Snowflake also provides built-in security and compliance features, making it a better fit for organizations with strict regulatory requirements, whereas Spark’s security setup depends on the platform it’s running on and often requires additional configuration. 10. Dremio Dremio is a strong alternative to Apache Spark due to its architecture and focus on simplifying data access and analytics. Dremio, eliminates the need for manual ETL (Extract, Transform, Load) processes by providing a self-service platform that enables users to query data directly from sources like cloud data lakes in real-time. Its integration with Apache Arrow and the use of in-memory acceleration leads to faster query speeds without the heavy computational overhead associated with Spark. Another key advantage of Dremio is its ease of use. Dremio’s intuitive interface allows non-technical users to explore and analyse data. Dremio also offers better cost efficiency by reducing data movement and optimising queries with features like data reflections, which cache query results to avoid repeated processing. These factors make Dremio a more user-friendly and cost-effective solution for businesses aiming to streamline data analytics without sacrificing performance.
Apache Spark is best for large-scale distributed data processing and real-time analytics. Here are 10 alternatives to Apache Spark you can consider for large scale data processing.
Apache Spark is best for large-scale distributed data processing and real-time analytics. Here are 10 alternatives to Apache Spark you can consider for large scale data processing.
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["AI", "machine learning", "ML", "analytics", "Aim", "RAG", "Apache Spark", "Elasticsearch", "Snowflake", "Python"]
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