Top 12 Best Hadoop Alternatives (Pros and Cons)

Top 12 Best Hadoop Alternatives (Pros and Cons). Is Hadoop loosing it’s main player popularity? If so, what are the best alternatives? In this article, we explore 12 best competitors. Whether you’re looking for a more streamlined data processing solution or a more specialized tool for a specific use cases, there are other tools available to consider. 

Well, Hadoop has been a dominant player in big data processing for many years, but it is not the only game in town. As the big data landscape continues to evolve, there are several Hadoop alternatives that offer unique advantages and use cases to assist you in solving big data challenges.

Let’s start Top 12 Best Hadoop Alternatives – (Pros and Cons).

Best Hadoop Alternatives

1. Firebird

Firebird is an open source relational database management system (RDBMS) originally based on the InterBase database. 

Well, Firebird is licensed under the Initial Developer’s Public License (IDPL). A variant of the Mozilla Public License (MPL). The source code is freely available, and developers modify and distribute it without any restriction. What is more, Firebird is a popular choice among developers who are looking for a powerful and reliable open source database system.

Pros of Firebird

  • Powerful, stable and technologically developed product.
  • Each database is located in one independent file, that is located anywhere on the disk.
  • Network access without the need of file sharing.
  • Easy backup, access rights management, transaction evaluation, replication, etc.
  • Free to use.

Cons of Firebird

  • A community project with no official support from the manufacturer. Any possible issues must be addressed individually by the designer. Some outdated documentation. 
  • Limited support for third-party tools, such as monitoring or visualization tools.

2. Cloudera

The next of Top 12 Best Hadoop Alternatives is Cloudera. Founded in 2008 by a group of engineers who helped develop Hadoop technology at Yahoo. 

The company’s flagship product is Cloudera Data Platform (CDP). Generally, it provides a unified data management and analytics platform. That enables organizations to store, process, and analyse big data across on premise, cloud, and hybrid environments. CDP includes a suite of tools and services for data engineering, data warehousing, data science, machine learning. All accessed through a unified user interface.

Pros of Cloudera

  • Use options to automate, build, and deploy machine learning and artificial intelligence.
  • Helps to build machine learning workflows by providing technology that simplifies data science.
  • Protects your business with risk modelling and analysis.
  • Help discover data across workloads.
  • Integrated suite with all analytics engines in one place.

Cons of Cloudera

  • Membership fees are expensive.
  • Cloudera is a complex platform that requires a high degree of technical expertise to manage and operate effectively. 

3. Apache Spark

Apache Spark is a distributed computing framework designed for large scale data processing and analytics. Also it provides an interface for programming clusters of computers. Making it possible to perform data processing tasks on large datasets using multiple computers in parallel.

Spark was developed as an alternative to Hadoop MapReduce to provide faster and more flexible processing capabilities. Based on Hadoop Distributed File System (HDFS). It integrates with other data sources such as Apache Cassandra, Apache HBase, and Amazon S3.

Pros of Apache Spark

  • Apache Spark is powerful.
  • Increased access to Big data.
  • Advanced analytics.
  • User friendliness.
  • Career demand.

Cons of Apache Spark

  • Small file issue.
  • Lack of real time processing.
  • No file management system.
  • Doesn’t suit multi user environment.
  • No automatic optimization process.

4. Apache Storm

Apache Storm is a free and open source distributed real-time computing system. Furthermore, Apache Storm does what Hadoop did for batch processing for real time processing. It makes it easy and reliable to process unlimited streams of data. Apache Storm is simple, can be used with any programming language, and is a lot of fun.

This Hadoop alternative has many use cases: Realtime analytics, online machine learning, continuous computation, distributed RPC, ETL, and more. 

Pros of Apache Storm

  • Allows real time stream processing.
  • Storm is unbelievably fast because it has enormous power of processing the data.
  • Has operational intelligence.
  • The distributed system ensures that data delivery happens in case of node downtime.

Cons of Apache Storm

  • Not suitable for smaller datasets.
  • No framework level support. Project development starts from scratch, making it difficult for new developers to pick up.
  • Does not have built in security features, such as encryption or access control, which makes it more vulnerable to security breaches. Users need to implement their own security mechanisms or use third party tools to ensure data protection and privacy.

5. Apache Flink

Apache Flink is an open source distributed stream processing platform. Designed to handle large amounts of streaming data with low latency and high throughput. Also used for batch processing, making it a versatile tool for a variety of data processing tasks.

Built on a powerful and flexible architecture that allows users to create complex stream processing applications easily. It supports a wide range of data sources, including Kafka, HDFS, and Amazon S3, and it has built in support for various streaming data formats.

Pros of Apache Flink

  • Low latency, high bandwidth processing of data streams.
  • Built in support for fault tolerance, meaning recovers from node failures and other types of errors without losing data or interrupting processing.
  • Automatic cost based optimizer.
  • Has a vibrant open source community that provides support, documentation and development resources for users.

Cons of Apache Flink

  • Only limited types of streaming data sources are supported.
  • Limited long term storage options.
  • Challenging to debug, especially for complex stream processing applications.

6. Apache Cassandra

Apache Cassandra is an open source distributed NoSQL database management system. Designed to handle large amounts of data on many standard servers, providing high availability with no single point of failure. While is highly scalable and fault tolerant, making it suitable for use in large scale, mission critical applications.

Provides low latency for both read and write operations. Is based on a decentralised architecture where data is distributed across multiple nodes to provide redundancy and fault tolerance.

Pros of Apache Cassandra

  • Easily scaled down or up.
  • Features data replication, so it’s fault tolerant and has high availability.
  • Peer to peer architecture rather than master slave architecture, so there isn’t a single point of failure.
  • High performance.

Cons of Apache Cassandra

  • Data is modeled around queries and not structure, resulting in the same information stored multiple times.
  • It doesn’t support ACID and relational data properties.

7. Google Cloud Dataflow

Google Cloud Dataflow is a fully managed service for building batch and streaming data processing pipelines. Hence, it enables the creation and execution of data processing flows using a simple programming model based on Apache Beam. A unified open source software. programming model for both batch and stream processing.

With Google Cloud Dataflow, you can perform data transformation tasks. Like filtering, aggregating and merging in large data sets. And then store the results in various data stores such as BigQuery, Google Cloud Storage or Google Cloud Spanner.

Pros of Google Cloud Dataflow

  • Flexible scheduling and pricing for batch processing.
  • Ready to use real time AI patterns.
  • Autoscaling of resources and dynamic work rebalancing.

Cons of Google Cloud Dataflow

  • Documentation is poor. 
  • Service can be expensive, especially for large and continuous data processing.
  • Although Google Cloud Dataflow supports many programming languages, it does not support all of them, which may be a problem for some development teams.

8. Apache NiFi

Apache NiFi is an open source data integration platform that provides users with a web based user interface. For designing, managing, and monitoring the flow of data between disparate systems, devices, and data sources. NiFi was developed by the US National Security Agency (NSA) and released as an open source project in 2014 by the Apache Software Foundation.

Users automate the flow of data between systems in real time. Provides a variety of processors, controllers and other tools for data routing, transformation and mediation. Additionally there is data source tracking, security features, and a powerful and scalable architecture for handling large amounts of data.

Pros of Apache NiFi

  • Provides security policies on user level, process group level and other modules too.
  • NiFi supports clustering, so it works on multiple nodes with same flow processing different data, which increase the performance of data processing.
  • Supports around 188 processors and a user also creates custom plugins to support a wide variety of data systems.

Cons of Apache NiFi

  • Apache NiFi have state persistence issue in case of primary node switch, which sometimes makes processors not able to fetch data from sourcing systems.
  • When node gets disconnected from NiFi cluster while a user is making any changes in it, then the flow.xml becomes invalid.Anode cannot connect back to the cluster unless admin manually copies flow.xml from the connected node.

9. Apache HBase

Apache HBase is a distributed, scalable, NoSQL database that is built on top of the Hadoop Distributed File System (HDFS). It is an open-source, column-oriented database that provides random, real-time access to large datasets.

Designed to handle big data workloads that require high write and read throughput, low latency, and real time queries. Particularly useful for applications that need to store and access large amounts of structured or semi-structured data, such as sensor data, log data, or social media data.

Pros of Apache Hbase

  • Low latency access to data.
  • MapReduce and Hive/Pig integration.
  • Auto failover and reliability.
  • HBase allows data compression and is ideal for sparse data.

Cons of Apache Hbase

  • No support SQL structure.
  • Memory issues on the cluster.

10. Google BigQuery

Another  choice of Top 12 Best Hadoop Alternatives is Google BigQuery. A fully managed, cloud based data warehouse and analytics platform offered by Google Cloud Platform. It is designed to help businesses and organizations to store and analyze large amounts of data in a quick and efficient manner, using a pay as you go pricing model.

Pros of Google BigQuery

  • Offers a scale friendly pricing structure.
  • Access the data you need on demand.
  • Flexible architecture speeds up queries.

Cons of Google BigQuery

  • Works best with flat tables, which makes managing an enterprise data model difficult.

11. Apache Druid

Apache Druid (previously known as Metamarkets Druid) is an open source, column oriented, distributed database designed for real time analytics on large datasets.  Handles high volumes particularly well, with high speed data ingestion and querying, whilst providing low latency and interactive data exploration.

Pros of Apache Druid

  • Combines stream and historical analytics.
  • Real time aggregations.
  • Batch and real time ingestion.

Cons of Apache Druid

  • Stores all data in memory for faster querying, which leads to high memory usage and costs.
  • May not integrate easily with all data processing and analytics tools, which may require additional development work to connect to other systems.

12. Apache Apex

Apache Apex is an open source, scalable, and high performance distributed stream processing platform that is designed to handle large volumes of data streams in real time. Provides low latency processing of large data sets, making it ideal for high speed data processing use cases.

Pros of Apache Apex

  • Provides a fault tolerant, distributed processing framework that is designed to handle failures and recover quickly from any system or hardware issues.
  • Easily deployed on a variety of environments, including on premise, in the cloud, or on hybrid environments. Runs on various operating systems and can be integrated with other Big Data technologies, such as Hadoop and Spark.
  • Has a growing community of users and contributors, providing a wealth of resources, support, and best practices for developing and deploying streaming applications.

Cons of Apache Apex

  • While Apache Apex provides support for SQL-like querying through its Apex StreamSQL language, it does not support all the features and functions of traditional SQL databases.
  • Resource intensive, especially when processing large volumes of data streams in real time. This leads to higher hardware requirements and costs.

Thank you for reading Top 12 Best Hadoop Alternatives – (Pros and Cons). We conclude this article blog. 

Top 12 Best Hadoop Alternatives (Pros and Cons) Conclusion

Whilst Hadoop has been the main choice for big data platform for many years, there are now many viable alternatives available in the market. Whether you are looking for a more modern platform that supports real time processing, a more scalable platform that handles larger datasets, or a more cost effective platform that reduces infrastructure costs, those are some of the best alternatives to Hadoop to fit your needs.

Choose one of the alternatives if you are after  modernizing your big data infrastructure, reducing costs, or gaining new insights from your data. 

Avatar for Kamil Wisniowski
Kamil Wisniowski

I love technology. I have been working with Cloud and Security technology for 5 years. I love writing about new IT tools.

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