The real challenge of big data processing is not only dealing with a massive amount of data but also processing it at high speed. Hence, here comes the demand for stream data processing and the framework which supports it. Apache Spark has received immense popularity as a game-changer in the big data world due to its streaming analytics and stream data processing features.
However, Apache Spark is not the one, there are many Apache Spark alternatives in the market that are also gaining popularity with more advanced features. These certifications are also gaining popularity along with the platform. Databricks certification is one of the best Apache Spark certifications. In this blog, we will discuss the best alternatives for Apache Spark from different viewpoints.
Want to become a certified Spark professional? Here are the 5 Best Apache Spark Certifications that will boost your career!
Comparable Features of Apache Spark with best known Apache Spark alternatives
Stream data processing has grown a lot lately, and the demand is rising only. There is a need to process huge datasets fast, and stream processing is the answer to this requirement. The components required for stream processing include an IDE, a server, Connectors, Operational Business Intelligence or Live data Mart and Streaming Analytics.
There are many new products and frameworks in the field of stream analytics and processing like IBM InfoSphere, SAG Apama, Apache Spark and Apache Storm that serve these purposes. These tools help the organizations to meet the changing real-time business conditions. These tools enable the teams to manage trading, system monitoring, fraud detection, etc. Real time data stream processing is acting like a game changer in the big data ecosystem containing Hadoop and other technologies.
Apache Spark has undoubtedly become a standard tool while working with Big data. It is way ahead of its competitors as it is used widely for all kind of tasks. The use cases of Stream processing offered by Spark include Data discovery and research, Data analytics and dashboarding, Machine learning, and ETL.
Some of the key features of Apache Spark are
- It is open source analytics platform for large-scale processing of huge datasets.
- It works as a high-speed engine with high performance in batch as well as streaming data.
- It has resilient distributed datasets (RDDs), and the in-memory data structure allows Spark to perform functional programming.
- It uses a DAG scheduler along with physical execution engine and the query optimizer.
Apache Spark is 100 times faster than Hadoop in terms of data processing. Here are the top 11 factors that make Apache Spark faster.
- It is based on the model of micro-batch with high latency.
- Spark recovers the lost work and avoids duplication of work by processing each record only once.
- Big batch calculations can be carried out by pinning memory as Spark streaming collects data streams in the form of mini-batches and runs the batch program.
- Spark is also very easy to use and implement by writing applications in Java, Python, R, Scala, SQL, etc.
- It contains a stack of libraries Spark SQL, MLlib (for machine learning), Spark Streaming, and GraphX.
Hence, it combines streaming, SQL, and complex analytics. Apache Spark effectively runs on Hadoop, Kubernetes, and Apache Mesos or in the cloud accessing a diverse range of data sources. It enjoys excellent community background and support. Also, there are some special qualities and characteristics of Spark including its integration and implementation framework allowing it to stand out. To know more about Apache Spark, you should learn about Spark RDD which is the fundamental data structure of Apache Spark.
But certain other products and frameworks can be seen as Apache spark alternatives. These tools have been growing fastly for years and have become industry leaders along with Spark.
Want to know how important is the Apache Spark in Big Data Industry? Read our previous blog on the importance of Apache Spark in Big Data industry.
Some of the best Apache Spark alternatives that are becoming direct competitors are
- Apache Storm
- Apache Flink
- SAS
- TIBCO StreamBase
- IBM InfoSphere Streams
- Software AG’s Apama
Let’s know the aspects of Apache Spark alternatives which can beat the competition
Apache Storm
It is one of the best and most popular Apache Spark alternatives. Apache Storm is the open source framework for stream processing created by Twitter. It is seen as a distributed real-time computation system that provides heavily scalable event collection. It contains other open source parts like Zookeeper, Kafka, and ZeroMQ.
Along with, it uses Zookeeper for cluster management, Amazon Kafka in case of queued messaging and ZeroMQ in case of multicast messaging. Furthermore, Apache Storm runs in several deployments in production. It makes the real-time processing of unbounded data streams easy with its many use cases like continuous computation, online machine learning, real-time analytics, ETL, distributed RPC, etc.
It is a high-speed platform that can be used simply with any programming language. Apache Storm uses Spouts, Blots, and Tuples for processing millions of tuples in every second for each node. The topology in Storm consumes the data streams and complexly processes them. Storm integrates with the database and queuing technologies we usually use. It is a fault-tolerant, scalable, an easy to operate and use platform.
Apache Flink
It is another platform considered one of the best Apache Spark alternatives. Apache Flink is an open source platform for stream as well as the batch processing at a huge scale. It provides a fault tolerant operator based model for computation rather than the micro-batch model of Apache Spark.
It uses streams in case of all workloads like SQL, micro-batch, streaming and batch. It is famous for its lightning speed of data processing. It means that all the data elements are pipelined immediately using the streaming program as soon as it receives them. Flexible window operations can be performed on these streams.
Eager to learn more about Apache Flink? Here is how Apache Flink is a new feather cap in Big Data Analytics!
Moreover, Apache Flink uses iterative transformations on collections and is optimized for iterative processes. It uses operator chaining, sorting, partitioning and joining algorithms for optimization. Also, Apache Flink allows strong compatibility with other tools, and you can use the code from Storm, MapReduce, etc. easily on the execution engine by Apache Flink. It is known for its excellent performance with its closed loop iterations that make graph processing and machine learning faster and effective. It requires minimum efforts in configuration and achieves high throughput along with low latency.
IBM InfoSphere Streams
It is also one of the best Apache Spark alternatives offered by IBM for the stream processing. It has all the typical features needed for stream processing use case implementation. It offers the integration abilities along with a highly scalable event server.
It has an Eclipse-based IDE that allows the visual configuration and development. InfoSphere has a better performance than Storm. It helps in uncovering patterns from the information in the form of data flows during the period (minutes or hours). Additionally, it can even fuse the streams that can help you in gaining insights from multiple streams.
The platform is of great value to the business with its fraud detection ability and network management features. It contains a runtime environment where deployment and monitoring of stream applications can be performed. There is a programming model where stream applications can be written using Streams Processing Language (SPL). There are some monitoring tools and interfaces for better administration.
TIBCO StreamBase
It is another high-performance stream processing system for building fast applications that can act and analyze real-time data. StreamBase offers the product that will support the developers who are building real-time applications, and they will be able to deploy them more quickly and easily.
Do these Apache Spark alternatives help to overcome the Apache Spark limitations? Read this blog till end to learn more!
TIBCO StreamBase has a LiveView data mart that consumes live data continuously streaming from real-time sources of data. It then creates an in-memory warehouse to store the data and later provides the push-based query outputs to the users. It also provides alerts to the end users. TIBCO StreamBase is the only product offering a live data mart, and no other vendor offers a similar product.
You can see the StreamBase LiveView Desktop as a push-based application which communicates with the live data mart. The users can analyze and use the streaming data, and there are visual elements for the interactive action. There are options to spot the real-time conditions which can be fraud, and the user can even stop the trading order. The desktop is like an interactive command application for the business users.
SAS
This Apache Spark alternative offers many solutions in the field of big data for managing the huge data sets effectively. These solutions include SAS Visual Analytics, SAS Visual Data Mining, and Machine Learning, SAS Grid Manager, SAS Econometrics, etc.
The high-performance analytics by SAS can work for distributed and in-memory data manipulation. The SAS in Memory Statistics offers interactive distributed data exploration, text analytics, classification or predictions, filtering and matrix factorization.
Conclusion
To conclude, knowing Big data is the trend of industry, and Apache Spark development is one of the lucrative fields that anyone can prosper and begin as a big data developer. You can get one of the best Spark certification to validate your knowledge and expertise, and become a Certified Big Data professional.
Whizlabs offers the industry-leading Hortonworks Spark developer certification (HDPCD) course. This is a brainstorming guide which perfectly meets academic knowledge with required hands-on exercises. Access the course online and become a certified Apache Spark professional for tomorrow.
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