Top Spark Alternatives 2024: Power Up Data Processing!

What Is Spark?

Apache Spark is a versatile data-processing framework that can aid in performing heavy-duty processing tasks on very large datasets. It is also capable of distributing data processing jobs among several machines, either alone or in combination with additional distributed computing technologies. This makes it a star for data companies for manipulation of big data and machine learning. Additionally, providing an intuitive API that takes away most of the tedious labor involved in big data processing and distributed computing relieves the developers of some of the programming responsibilities associated with these activities.

Apache Spark was developed in the AMP Lab at U.C. Berkeley in 2009. It offers native bindings for the computer languages Python, R, Java, Scala etc. It also comes with many libraries to help with stream processing [Spark Streaming], graph processing [GraphX], and machine learning [MLlib] application development. 

Use Cases of Spark

In general, Spark works best when there is a tight deadline. Numerous data processing jobs can be handled with Apache Spark.

Real-Time Analysis And Processing of Data

Spark can be used for near-real-time data processing. The suggested method for managing streaming data in Apache Spark is called "structured streaming." This can be used to read tweets in real time and analyze their sentiment.

Machine Learning

Spark MLlib utilizes massive data sets to train machine learning models, which are subsequently implemented in your applications. It offers prebuilt machine learning algorithms for applications like pattern mining, collaborative filtering, clustering, regression, and classification. This is highly used to predict customer trends based on the data generated during their online activity.

Processing of Graph Type Data

Social networks and many other data networks contain graph-structured data, and Spark aids their analysis. Spark GraphX can be used to find the difference between two points in a graph. 

Features of Spark

Spark is at the top of the game because of its distinctive features:

High Tolerance to Faults

It is particularly designed to avoid various node failures. It makes use of DAG and RDD (Resilient Distributed Datasets) to achieve this fault tolerance. DAG is the lineage of steps of all the operations and transformations to complete a task without a hitch. In case of node failure, the existing steps in DAG can be executed again to achieve the same results.

Highly Dynamic

Spark provides 80 high-level operators, which makes it a go-to platform to develop similar and parallel platforms and applications. 

Lazy Evaluation in Apache Spark

While working on various programming languages, there are a lot of transformations that are not then evaluated by Spark. The changes made in Spark RDD undergo lazy evaluation, and thus, the result will not be delivered soon. 

Rather, it forms an entirely new RDD from the existing ones, enhancing efficiency. The final computation or results are only accessible when actions are called, and the transformations are then added to the DAG. Because the Spark engine can see every transformation before acting on it, Spark can make optimization judgments.

Reusability

The code written in Spark can be reused for batch processing, compiling streaming data against historical data or running multiple ad-hoc queries on the streaming state.

In-Memory Computing

Apache Spark can process tasks in memory and does not need to write back intermediate results to the disk, in contrast to Hadoop MapReduce. This increases the speed of Spark processing by multiple folds. 

Furthermore, Spark can cache the intermediate findings for use in the following cycle. Because a common dataset may be used for many tasks or because findings from one phase can be used in another, Spark gains an additional performance boost for iterative and repeated processes.  

Apache Spark Alternatives 2024

With its key capabilities and features in streaming analytics and stream data processing, Apache Spark has been transforming the big data industry. An IDE, a server, a live data mart, streaming analytics, and connectors are the essential components. 

Although Apache Spark is fantastic and widely used, there are a lot of excellent alternatives to Spark that work just as well. These tools have demonstrated their ability to provide effective team management, system monitoring, fraud detection, real-time stream processing, and other features.

Top 15 Spark Alternatives

Here is a list of the Apache Spark alternatives for you to choose from:

1. Apache Storm

One of the top competitors is Apache Storm. It is a free-of-cost, distributed, open-source stream processing computation system that helps to reliably process infinite streams of data in real-time. Written in the Clojure programming language, it can be easily set up and operated, thus making it really popular among beginners. The heavy-duty processing of data in each node is done using Spouts, Blots and Tuples. 

Using Apache Storm is easy and enjoyable, and it works with any programming language. It supports a wide range of scenarios, including online machine learning, continuous computing, real-time data analytics, ETL, etc.

When using an Apache Storm topology, data streams are consumed and processed in a variety of wildly complex ways. The streams are repartitioned as needed between each computational step. 

Significant Characteristics:

• Scalability
• Fault Tolerance
• Management of clusters
• Seamless integration with multiple databases.
• Multicast messaging 

2. Apache Flink

One capable platform that is an alternative to Spark is Apache Flink. It provides an operator-based, fault-tolerant calculating methodology and is open-source. Streams are used in workload operations so that the streaming application may rapidly pipeline all of the components. It can compute both types of unbounded data and bounded data (Data having a definite start and end). 

The framework was developed to operate in all popular cluster setups and carry out calculations at any size and speed of memory. It handles batches as finite-bordered data streams and integrates with Apache Hadoop, Spark, HBase, MapReduce, etc. Flink's rich feature set lets it create and execute various applications.  

Significant Characteristics:

• High throughput and Low Latency
• Streaming and batch processing support of a streaming processor
• Expandability to thousands of nodes in multiple clusters 
• Semantics of event-time processing
• Processing data at a very quick rate 

3. Apache Hadoop

Apache Hadoop is an alternative Spark system that uses a straightforward programming approach to allow collaborative processing of massive data collections on computer clusters. It is a collection of free and open-source tools for efficiently storing and handling massive datasets, ranging in size from gigabytes to petabytes. 

With each machine providing local computing and storage, it is built to scale up from a single server to thousands of units and a wide network of computers to manage the problems regarding data and computation.

Significant Characteristics:

• Easy to use and affordable
• Tolerance for faults
• Adaptable and very easily accessible
• Utilizes Data Locality
• Quicker processing of data

4. Lumify

A well-liked tool for big data fusion, analysis, and visualization that aids in the creation of actionable intelligence is called Lumify. It is a tool that enables intelligence analysts to make prompt, well-informed decisions that are necessary for our country's security. Users may also explore a variety of links and make intricate connections in their data by using this big data tool. 

Significant Characteristics:

• Real-time collaborative workplaces
• Quick and well-informed decision-making
• Dynamic histograms
• Geographic landscapes that are interactive

5. Google BigQuery 

BigQuery is one of the big data analytics web services hosted in the cloud that handles enormous amounts of read-only data. It is a warehouse that is handled serverless by Google and manages petabyte-scale data. It observes how the PaaS model uses ANSI SQL to support query processing.

Significant Characteristics:

• Simple to incorporate with additional machine learning technologies 
• Complete assistance provided by the Google Cloud Platform
• Efficient when employing geographic analysis
• Seamless integration with other Google products like Google Analytics

6. Apache Sqoop

It is an Apache Spark alternative tool for transferring data between mainframes or relational databases and Hadoop. Using Hadoop Distributed File System (HDFS), developers can import data from a mainframe or relational database management system (RDBMS) like MySQL or Oracle. They can then use Hadoop MapReduce to change the data and export it back into an RDBMS.

Significant Characteristics:

• Efficient data transfer between Apache Hadoop and structured data stores
• Supports incremental loads of a single table or a free-form SQL query
• Allows parallel data transfer for improved performance
• Supports data import into HDFS, Hive, and HBase, and export from Hadoop to external databases

7. Snowflake

Since it is a single Spark alternative platform that supports numerous workloads without data silos, Snowflake enables the most important workloads. It creates data-intensive applications and is used by businesses worldwide as it provides accurate and timely data accessibility from a reliable source. 

Significant Characteristics:

• Works efficiently on Google Cloud Platform, Azure, and Amazon S3
• Effective performance
• Seamless data sharing 
• Strong client and community support 
• Effortless connection with Tableau, Sigma, Qlik, and other BI and data integration tools. 

8. Dremio

Dremio is a popular and simple data lakehouse platform and a well-known Spark alternative. It provides quick querying capabilities along with a self-service layer for the storage units. With a central data catalog for all linked data sources, it is a data intake platform where querying the data lake storage using various skills, such as predictive pipeline lining, is done. It is an innovative open-source Data-as-a-service (DAAS) platform.

Significant Characteristics:

• Excellent support for a variety of data sources, including NoSQL and Hadoop.
• Enabling users to be independent and effective
• Quick extraction and processing of data
• Capacity to establish a connection using Python, SQL Live, or any BI tool
• Optimizing queries with native pushdowns

9. Splunk

It is a great, well-known platform and can be utilized for machine data visualization, analysis, monitoring, and search. With simple communication, it improves the experience of linked devices. In a hybrid context, it enables integrated security, observability, and custom programs. 

Significant Characteristics:

• Superior understanding of everyday operations
• Adaptability and expandability
• Secure and visually appealing displays
• Instantaneously, urgent notifications
• Easily searchable and equipped with dashboards, graphs, reports, and alerts 

10. Elasticsearch

Elasticsearch was released in 2010 and is a great alternative to Spark. It is a well-known, decentralized, open-source search and analytics tool for all kinds of data, including numerical, textual, geographical, structured, and unstructured information. It provides an extensive full-text search engine with an HTTP interface JSON documents, and depends on the Apache Lucene library. 

Significant Characteristics:

• User-friendly interface REST APIs
• Increased speed
• Scalability in a horizontal manner 
• Searchable images 
• Automatic data rebalancing and node recovery 

11. Presto

Presto is an open-source distributed SQL query engine for executing interactive analytical queries across data sources of any size. It was built from the bottom up for interactive analytics and approaches the speed of commercial data warehouses while scaling to companies like Facebook.

Significant Characteristics:

• Quicker analysis
• Handles both NoSQL and traditional databases
• Operates both in the cloud and on-premises
• Decentralized SQL engine stored in memory

12. IBM InfoSphere Streams

InfoSphere Streams, a well-known software platform that helps create and run applications using data streams, is powered by IBM. Its highly scalable event server gives it integration competencies. 

Significant Characteristics:

• Runtime environment for stream application deployment and monitoring
• Improved data connections
• The use of SPL, or stream processing language
• Excellent development assistance

13. Sprint Boot

Spring Boot is a free and open-source Java framework that assists programmers in building self-contained, production-ready Java applications and web services. There is no need for complex XML setups or the manual development of boilerplate code. 

Significant Characteristics:

• By bootstrapping, memory space is saved.
• Create microservices using microframeworks.
• Strong community participation

14. TIBCO StreamBase

StreamBase (TIBCO Streaming), a common event processing and computing platform that leverages relational and mathematical handling of real-time data streams, is powered by TIBCO. It features a LiveView data mart that gathers real-time data that is continuously streaming from various data sources. 

Significant Characteristics:

• Basic and combined operations 
• Eclipse-powered IDE for EventFlow, a graphical development language 
• Integrated native clustering for dispersed scaling

15. Amazon EMR

Amazon EMR (Amazon Elastic MapReduce) is a well-managed cluster and cloud big data platform for running SQL queries, machine learning applications, and large-scale distributed data processing tasks.  

Significant Characteristics:

• Supports strong tools such as Hadoop, Spark, and others. 
• Adaptable data repositories 
• Affordability in data processing

Spark Performance Optimizations Best Practices

A Spark job can be optimized using many techniques:

Serialization

Spark uses JAVA serializer as default but can also support Kryo serializer. It is 10 times faster than the Java serializer and has a compact binary format. 

Selection of API

RDD, DataFrame, and DataSet are the three forms of API that Spark is compatible with. RDD- for low-level operations; Dataframe- makes a plan for query; Dataset- very type-safe and incorporates the encoder into their serialization process.

Broadcast Variable

Small datasets are locally accessible on nodes via the broadcast variable. 

Persist and Caching

When any software has to store a small amount of repeatedly used data, the persist and cache techniques store the data in memory.

Want to begin your career as a Big Data Engineer? Then get skilled with the Big Data Engineer Certification Training Course. Register now.

Conclusion

At scale, Apache Spark has been having a significant impact on the entire data science and engineering spectrum. If you need to make an impression in interviews in the Big Data ecosystem and Spark concepts, you can enroll in the Big Data Hadoop Certification Training Course offered by Simplilearn. Enroll in the immersive learning experience and learn all the basics of Apache from excellent mentors 

FAQs

1. Is there anything better than Spark?

There are lots of spark alternatives that are mentioned above in this article; Apache Hadoop and Apache Flink are the top ones.

2. Which is better, Hadoop or Spark?

Compared to Hadoop, Spark is a more sophisticated technology since it processes data using artificial intelligence and machine learning (AI/ML).

3. Why look for alternatives to Spark?

Spark needs to be manually installed and configured because they are not ready right out of the box. Also, there is a price for quick processing rates, thus the need for an Apache Spark alternative.

4. How do Spark alternatives compare in terms of speed?

Every time Spark executes a task, input-output overhead is not a concern and thus performs better in terms of processing speed. 

5. What are the main features to look for in a spark alternative?

Speed, multi-language support, and advanced analytics are certain features that are required for an Apache Spark alternative.