September 26, 2022
March 22nd, 2023
Big Data is hogging the world with its immense capability of handling huge amounts of data and dealing with high-speed processing. The need for stream data processing is increasing and one technology that has proven its worth is Apache Spark.
Apache Spark has been revolutionizing the world of Big Data with its salient stream data processing competencies and streaming analytics. The major elements needed are connectors, a server, IDE, live data mart, and streaming analytics.
Apache Spark is wonderful and popular, but many other Apache Spark alternatives have been offering great results. These tools have been able to offer successful management of teams, monitoring of systems, detecting frauds, real-time stream processing, etc.
Before we explore the different alternatives to Apache Spark, let us glance through what Apache Spark is and its salient features.
Apache Spark is a multi-language engine for executing data engineering, data science, and machine learning on single-node machines or clusters.
Powered by the global giant, Apache Spark is an open-source, general use, unified framework, and analytics engine meant for big data and large-scale data processing. Spark has its own independent processes and a streaming API that empowers continuous processing via short interval batches.
It is a fast general processing engine that is fit for distributed data processing. Data scientists and engineers prefer working with Spark as it has a robust, flexible engine. It operates batch, streaming, or machine learning workloads that need fast availability of huge datasets.
Apache Storm is one of the key Apache Spark competitors. It is a free, distributed, open-source, stream processing computation system that assists in processing unbounded data streams with reliability. It is written in the Clojure programming language. It is easy to set up and operate even for novices. It makes use of Spouts, Tuples, and Blots for heavy processing in each node.
It caters to many scenarios like real-time data analytics, continual computation, online machine learning, ETL, etc. It parallelizes task computation and seamlessly integrates with other database technologies. The Storm topology effectively processes the data streams that are consumed.
Apache Hadoop, as an Apache Spark alternative, is an assortment of open-source utilities that effectively store and process large datasets that range from gigabytes to petabytes of data. It makes use of a wide network of computers for solving problems regarding data and computation. There is a robust software framework for distributed storage with the MapReduce model.
It empowers clustering many computers together for better analysis of huge datasets simultaneously. It can easily scale from individual servers to multiple machines, each having a storage and computation facility. It has its own file distribution system – HDFS (Hadoop Distributed File System).
Lumify is well-known for its big data fusion, analytics, and visualization capabilities. It empowers users in finding complicated connections and developing actionable intelligence. It helps in discovering different data relationships via a well-defined pack of analytical tools like collaborative workspaces, graph visualization, dynamic histograms, etc.
It offers full text faceted search, and interactive geospatial views in real-time to make the most of the data that is collected. Users can take quick and intelligent decisions based on the tool and its output, for the best business results.
Snowflake is one of the known Apache Spark alternatives. It facilitates the most critical workloads since it is one platform, with many workloads with no data silos. It makes data-intensive applications and is leveraged by organizations globally. It offers precise and quick availability of data through a consistent source.
It presents smooth integration with other BI and data integration tools like Tableau, Sigma, Qlik, etc. It works efficiently on Google Cloud Platform, Azure, and Amazon S3. It decreases the administration requirements of traditional warehousing solutions and requires no infrastructure to be handled.
Interesting Read: Teradata vs Snowflake: Two Data Warehousing Solutions Often Compared
Driven by Google, BigQuery is a multi-cloud data warehouse that is responsive and scalable. It is a totally managed and serverless data warehouse. It is recognized as one of the major Apache Spark competitors. It observes the PaaS model supporting queries through ANSI SQL.
It enables the assessment of petabytes of data and owns inbuilt machine learning abilities. It helps companies to implement business analytics with scalability. It integrates well with other Google products like Google Analytics.
TreasuryPay offers instantaneous enterprise data and intelligence through its product stack. It offers a great deal of transparency and visibility into heaps of transaction data, from anywhere, anytime. Through a single network connection, users can avail details of all types of information – supply chain and logistics, marketing, liquidity, etc.
It is one of the most innovative intelligence platforms and offers instant cognitive and accountancy services. It gives enriched information for your entire organization in real time with actionable intelligence.
As a known Spark alternative, Dremio is a popular and easy data lakehouse platform that offers fast querying capabilities with a self-service layer to the storage units. It is a data ingestion tool with a central data catalog for all connected data sources. It is easy to query the data lake storage with different competencies like Predictive Pipelining etc.
Dremio is an innovative type of data analytics platform that does not ask for cubes, warehouses, ETL for showcasing self-service analytics. It is an open-source Data-as-a-service (DAAS) platform.
Elasticsearch is a known search engine that is open source, distributed, and has a search and analytics capability. It depends on the Apache Lucene library and offers a comprehensive full-text search engine with an HTTP interface and JSON documents. It caters to a variety of data which could be numerical, textual, structured, unstructured, geospatial, etc.
It is popular for its simple-to-use REST APIs, speed, scalability, and distributed nature. It can be utilized for security intelligence, log analytics, operational intelligence, infrastructure metrics, geospatial analytics, container management, application performance monitoring, etc.
Splunk is a well-known platform in the Big Data category, for operational intelligence. It is leveraged for searching, monitoring, analyzing, and visualizing machine data. It enhances the experience of connected devices with easy communication. It empowers integrated security, observability, and custom apps in a hybrid environment.
It offers indexing and correlation of data in containers making it easily searchable with a generation of reports, graphs, dashboards, and alerts. It converts data into action with real-time alerts.
Presto is a renowned, fast, trustworthy SQL engine for data analytics and the Open Lakehouse. As an effective Apache Spark alternative, it executes at a large scale, with accuracy and effectiveness. It is an open-source, distributed engine to execute interactive analytical queries with disparate data sources. It has an efficient engine that can be designed for interactive analytics.
It has been liked and implemented by popular organizations like Uber, Intel, Facebook, Alibaba, etc. It empowers users to query data for insightful analytics from wherever it resides with a help of a single query that can fetch data from disparate sources. The analytics is fast and accurate.
Amazon EMR stands for Amazon Elastic MapReduce. It is a popular cloud big data and managed cluster platform for executing large-scale and distributed data processing activities, machine learning apps, and SQL queries. It offers simplification in executing big data frameworks like Hadoop, Spark, Hive, etc.
It provides execution of petabyte-scale analytics in a cost-effective manner. It offers spinning of clusters for short executing jobs and processes huge amounts of data in a scalable manner.
Apache Flink is a competent platform that is considered a good Spark alternative. It is open-source and offers a fault-tolerant, operator-based model for calculations. It makes use of streams in workload operations through which all components are pipelined instantly by the streaming program.
It seamlessly integrates with Apache Hadoop, Spark, HBase, MapReduce, etc. It provides in-memory management that can be tailored for effective computation. It has great fault-tolerant capabilities and flexible Windowing features.
Powered by IBM, InfoSphere Streams is a recognized software framework that assists in developing and executing applications through data streams. It has integration competencies through a highly scalable event server. There is an Eclipse-based IDE that empowers visual development and configuration.
Since it has good fraud detection capabilities and great network management features, it has been of supreme importance to developers. Patter discovery can be easily done from the collected information. Streams can even be fused for garnering insightful information from different streams.
Spring Boot is an open-source, Java framework that helps developers in creating independent and ready-to-use, production-grade Java applications and web services. It is apt for the large enterprise arena wherein it makes use of a microframework for creating microservices.
It needs minimum configuration to set up and hence is easy to learn and execute. There is no involvement of manual writing of boilerplate code or complicated XML configurations. It can seamlessly integrate with other products and offers a great connection with other databases.
Powered by TIBCO, StreamBase (TIBCO Streaming) is a popular event processing, and computing platform to utilize relational and mathematical handling of real-time data streams. It is ideally meant for the high-volume performance needs for streaming applications in the real-time environment.
It has a LiveView data mart that takes up live data that is streaming regularly from the data sources that have real-time data. There is an in-memory warehouse for the storage of data with a push-based query option.
Spark is strong, robust, popular, scalable, and general purpose, but it has its own set of limitations, and one size cannot fit all. Amongst the Spark alternatives we went through, each of them has its own periphery of expertise.
Hence, based on what we need, we must go through the list of possible alternatives to Apache Spark that can offer similar service offerings. Different parameters like costs involved, project deadlines, skilled resources, organizational objectives, etc. can be decisive in choosing the right Spark alternative.
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