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15 Spark Alternatives For Effective Data Analytics


September 26, 2022


March 14th, 2024

Big Data is hogging the world with its immense capability of handling vast 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 significant elements needed are connectors, a server, an IDE, a live data mart, and streaming analytics.

Apache Spark is excellent and famous, but many other Apache Spark alternatives have been offering excellent results. These tools have provided successful team management, system monitoring, fraud detection, real-time stream processing, etc.

Before we explore the different alternatives to Apache Spark, let us look at what Apache Spark is and its salient features.

What is Apache Spark?

Powered by the global giant Apache Spark, it is an open-source, general-use, unified framework and analytics engine meant for big data and large-scale data processing. Spark has independent processes and a streaming API that empowers continuous processing via short-interval batches.

It is a fast general processing engine suitable for distributed data processing. Data scientists and engineers prefer working with Spark because of its robust, flexible engine. It operates batch, streaming, or machine learning workloads that require the fast availability of massive datasets.

Apache Spark Features

  • Ease of writing applications in Java, Python, R, etc.
  • Availability in various data sources
  • Open-source, scalable, fault-tolerant
  • Implemented in JVM-based languages
  • Offers real-time analytics,
  • High-speed engine for high-performance
  • Resilient Distributed Datasets (RDD) and in-memory data structure
  • Competence to carry out extensive batch calculations
  • Variety of libraries like GraphX, MLlib, Spark SQL, etc.

Companies Using Apache Spark

  • Amazon
  • eBay
  • Yahoo
  • Netflix
  • Google
  • Hitachi Solutions
  • Groupon
  • Tencent
  • TripAdvisor
  • Yandex
  • Inspur
  • Databricks
  • Autodesk
  • UC Berkeley AMPLab

15 Spark Alternatives Ideal For Big Data Processing

  1. Apache Storm

    Apache Storm is one of the key Apache Spark competitors. It is a free, distributed, open-source stream processing computation system that reliably processes unbounded data streams. It is written in the Clojure programming language. It is easy to set up and operate, even for novices. It uses 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.

    Key Features

    • Fault tolerance
    • Highly scalable event collection
    • Cluster management
    • Queued and multicast messaging
    • Easy integration with databases
  2. Apache Hadoop

    Apache Hadoop, an Apache Spark alternative, is an assortment of open-source utilities that effectively store and process large datasets ranging from gigabytes to petabytes. It uses a vast computer network to solve data and computation problems. The MapReduce model provides a robust software framework for distributed storage.
    It empowers clustering many computers together for better analysis of massive datasets simultaneously. It can quickly scale from individual servers to multiple machines, each with a storage and computation facility. It has its own file distribution system—HDFS (Hadoop Distributed File System).

    Key Features

    • Cost-effective and easy to use
    • Fault tolerance
    • Flexible and highly accessible
    • Makes use of Data Locality
    • Faster data processing
  3. Lumify

    Lumify is well-known for its big data fusion, analytics, and visualization capabilities. It empowers users to find complicated connections and develop actionable intelligence. It helps discover different data relationships via a well-defined pack of analytical tools, such as collaborative workspaces, graph visualization, dynamic histograms, etc.

    It offers real-time full-text faceted search and interactive geospatial views to make the most of the data collected. Users can make quick and intelligent decisions based on the tool and its output for the best business results.

    Key Features

    • Collaborative workspaces in real-time
    • Fast, informed decision-making
    • Dynamic histograms
    • Interactive geospatial views
  4. Snowflake

    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 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.

    Key Features

    • Unlimited scalability
    • Effective concurrency and performance
    • Seamless sharing of data
    • Database replication and failover
    • Robust community and client support
  5. Google BigQuery

    Driven by Google, BigQuery is a responsive and scalable multi-cloud data warehouse managed and serverless. It is recognized as one of the significant Apache Spark competitors. It observes the PaaS model, supporting queries through ANSI SQL.

    It enables the assessment of petabytes of data and has built-in machine-learning abilities. It helps companies implement business analytics with scalability and integrates well with other Google products, such as Google Analytics.

    Key Features

    • Cost-effective with geospatial analysis
    • Comprehensive support from the Google Cloud Platform
    • Easily integrable with other machine learning technologies
    • Flexible and reasonable storage competencies
    • Automated backups and database scalability
  6. TreasuryPay

    TreasuryPay offers instantaneous enterprise data and intelligence through its product stack. It provides excellent transparency and visibility into heaps of transaction data 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 provides enriched information for your entire organization in real-time with actionable intelligence.

    Key Features

    • Single global connection to different processors and data types
    • Actionable business intelligence immediately for upcoming business decisions
    • Reduced risk of unauthorized access and fraud
    • Robust data and file activity monitoring
    • Dual factor authentication
  7. Dremio

    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.

    Dremio is an innovative data analytics platform that does not ask for cubes, warehouses, or ETL to showcase self-service analytics. It is an open-source Data-as-a-service (DAAS) platform.

    Key Features

    • Making users self-sufficient and productive
    • It dramatically supports multiple data sources like Hadoop, NoSQL, etc.
    • Capability to connect with any BI tool, SQL Live, or Python
    • Query optimization with native push-downs
    • Fast data processing and extraction
  8. Elasticsearch

    Elasticsearch is a known open-source, distributed search engine with 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 various data, such as numerical, textual, structured, unstructured, geospatial, etc.

    It is famous 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.

    Key Features

    • Resilience and scalability
    • Automated node recovery and data rebalancing
    • Horizontal scalability
    • Cross-cluster replication
    • Searchable snapshots
  9. Splunk

    Splunk is a well-known platform for operational intelligence in the Big Data category. It is leveraged for searching, monitoring, analyzing, and visualizing machine data. It enhances the experience of connected devices with accessible communication. It empowers integrated security, observability, and custom apps in a hybrid environment.

    It indexes and correlates data in containers, making it easily searchable with reports, graphs, dashboards, and alerts. It also converts data into action with real-time alerts.

    Key Features

    • Secure, attractive displays
    • High-end visibility into daily operations
    • Real-time, critical alerts
    • Quick analysis of metrics data
    • Flexibility and scalability
  10. Presto

    Presto is a renowned, fast, trustworthy SQL engine for data analytics and the Open Lakehouse. As an effective Apache Spark alternative, it executes on a large scale accurately and effectively. It is an open-source, distributed engine that executes interactive analytical queries with disparate data sources. It has an efficient engine that can be designed for interactive analytics.

    Popular organizations like Uber, Intel, Facebook, and Alibaba have liked and implemented it. It empowers users to query data for insightful analytics from wherever it resides with the help of a single query that can fetch data from disparate sources. The analytics is fast and accurate.

    Key Features

    • Faster analytics
    • In-memory distributed SQL engine
    • Supports relational and NoSQL databases
    • Executes on-premises and in the cloud
    • Supports data lakes, warehouses, and access to multiple connectors
  11. Amazon EMR

    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 simplifies executing big data frameworks like Hadoop, Spark, Hive, etc.

    It cost-effectively provides execution of petabyte-scale analytics. It offers spinning of clusters for short executing jobs and processes vast amounts of data in a scalable manner.

    Key Features

    • Offers elasticity and engines to execute analytics
    • Supports powerful tools like Spark, Hadoop, etc.
    • Reduces the cost of processing extensive data
    • Allows to provide as much capacity quickly and easily
    • Flexible data stores
  12. Apache Flink

    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 uses 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 practical computation. It has excellent fault-tolerant capabilities and flexible Windowing features.

    Key Features

    • Data processing at a breakneck speed
    • Low latency and high throughput
    • Access to APIs in Scala, Python, and Java
    • Batch processing and streaming through a streaming processor
    • Scalability up to thousands of nodes in a cluster
  13. IBM InfoSphere Streams

    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.

    It has been of supreme importance to developers since it has good fraud detection capabilities and network management features. Pattern discovery can be easily made from the collected information. Streams can even be fused to garner insightful information from different streams.

    Key Features

    • Runtime environment for deployment and monitoring of stream apps
    • Usage of Streams Processing Language (SPL)
    • Facilitates constant and fast assessment of vast volumes of data
    • Enriched data connections
    • Good development support
  14. Sprint Boot

    Spring Boot is an open-source Java framework that helps developers create independent, ready-to-use, production-grade Java applications and web services. It is apt for the large enterprise arena, which uses a micro-framework to develop microservices.

    It needs minimum configuration and is easy to learn and execute. There is no involvement in the manual writing of boilerplate code or complicated XML configurations. It can seamlessly integrate with other products and offers a great connection with different databases.

    Key Features

    • Banner customization
    • Saving memory space through bootstrapping
    • Lessened boilerplate code
    • Good community support
    • No need for any XML configuration
  15. TIBCO StreamBase

    Powered by TIBCO, StreamBase (TIBCO Streaming) is a popular event processing and computing platform that utilizes the relational and mathematical handling of real-time data streams. It is ideally meant for the high-volume performance of streaming applications in the real-time environment.

    It has a LiveView data mart that takes live data streaming regularly from real-time data sources. There is also an in-memory warehouse for data storage with a push-based query option.

    Key Features

    • Native clustering built for distributed scaling with fault tolerance
    • Graphical development language, EventFlow, utilizing Eclipse-powered IDE
    • Well-designed for real-time, fast processing
    • Simple and aggregate functions
    • Low latency, high throughput, complicated event handling

Summing It Up

Spark is strong, robust, popular, scalable, and general-purpose but has limitations, and one size does not fit all. Among the Spark alternatives we reviewed, each has its periphery of expertise.

Hence, based on our needs, we must review the possible alternatives to Apache Spark that offer similar service offerings. Different parameters like costs, project deadlines, skilled resources, organizational objectives, etc., can be decisive in choosing the suitable Spark alternative.

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