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15 Spark Alternatives For Effective Data Analytics


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.

What Is Apache Spark?

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 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 big 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


Leading Spark Alternatives Ideal For Big Data Processing

  • Apache Storm
  • Apache Hadoop
  • Lumify
  • Snowflake
  • Google BigQuery
  • TreasuryPay
  • Dremio
  • Elasticsearch
  • Splunk
  • Presto
  • Amazon EMR
  • Apache Flink
  • IBM InfoSphere Streams
  • Sprint Boot
  • TIBCO StreamBase

Apache Storm:

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.

Key Features:
  • Fault tolerance
  • Highly scalable event collection
  • Cluster management
  • Queued and multicast messaging
  • Easy integration with databases

Apache Hadoop:

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).

Key Features:
  • Cost-effective and easy to use
  • Fault tolerance
  • Flexible and highly accessible
  • Makes use of Data Locality
  • Faster data processing


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.

Key Features:
  • Collaborative workspaces in real-time
  • Fast, informed decision making
  • Dynamic histograms
  • Interactive geospatial views


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

Key Features:
  • Unlimited scalability
  • Effective concurrency and performance
  • Seamless sharing of data
  • Database replication and failover
  • Robust community and client support

Google BigQuery:

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.

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


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.

Key Features:
  • Single global connect 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


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.

Key Features:
  • Making users self-sufficient and productive
  • Great support for 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


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.

Key Features:
  • Resilience and scalability
  • Automated node recovery and data rebalancing
  • Horizontal scalability
  • Cross-cluster replication
  • Searchable snapshots


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.

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


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.

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

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 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.

Key Features:
  • Offers elasticity and engines to execute analytics
  • Supports powerful tools like Spark, Hadoop, etc.
  • Reduces cost of processing large data
  • Allows to provide as much capacity quickly and easily
  • Flexible data stores

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 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.

Key Features:
  • Data processing at a very fast 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

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.

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.

Key Features:
  • Runtime environment for deployment and monitoring of stream apps
  • Usage of Streams Processing Language (SPL)
  • Facilitates constant and fast assessment of huge volumes of data
  • Enriched data connections
  • Good development support

Sprint Boot:

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.

Key Features:
  • Banner customization
  • Saving memory space through bootstrapping
  • Lessened boilerplate code
  • Good community support
  • No need for any XML configuration

TIBCO StreamBase:

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.

Key Features:
  • Native clustering inbuilt 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
Spark Alternatives: Summing It Up

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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