Big Data – one of the most demanded niches in enterprise software development, has gained high popularity due to the rapid and constant data volume growth. The big data tools and techniques in the market are very competitive and full of software that performs almost similar work. Several data processing engines are getting a lot of use in this particular tech stacks. Although there are numerous frameworks out there in this context today, only a few are very popular and demanded. These frameworks provide some of the most popular tools used to carry out common Big Data-related tasks. They help to store, analyze, and process the data. Now, definitely, the question arises here what the best pick is as a framework in 2020? In this article, we have considered 5 of the top Big Data frameworks expected to hold positions in the upcoming 2020.
Let’s find out!
What are the best Big Data frameworks in 2020?
The present market is full of great big data tools. Most of them are of excellent performance. Not all of them are covered here. However, I have tried to cover the most prominent ones. Here is a list that will be included here:
1. Hadoop
2. HBase
3. Apache Spark
4. Apache Flink
5. Apache Impala
Let’s have a look!
1# Hadoop Big data – the show will go on
This open-source Apache product Hadoop is a revolutionary solution in this technology space since its inception. Interestingly, most bigdata software is built-in compliant with the Hadoop or run around it. This scalable, distributed framework can store and process petabytes of data at a time. Hadoop architecture is based on master-slave topology, where only one master node and multiple data nodes conduct the whole mechanism. Hadoop resolves the modern database’s memory issue by using an intermediary layer between data storage and interactive database.
There are three major layers in Hadoop architecture –
- HDFS (Hadoop Distributed File System) – Storage layer
- MapReduce – Data processing layer
- Yarn – Resource management layer
HDFS is the central data storage unit of Hadoop, which splits data and store into blocks. It has got two daemons running at the backend – NameNode and DataNode. These nodes are nothing but servers where NameNode is the master server that regulates the files and namespaces, and DataNodes are slave servers that handle actual business data. HDFS is highly fault-tolerant, and that happens through its replication management. Besides, it uses rack awareness algorithms for replication management.
http://hadoop.apache.org/docs/r1.2.1/hdfs_design.html
Data processing in Hadoop happens through MapReduce. This is itself a software framework that allows writing applications for processing a large amount of data. MapReduce runs these applications in parallel. MapReduce job comprises of several map tasks and reduces tasks. As the data segregates in Hadoop into several parts, each map and reduce task works on the part of data. This is a unique load distribution across the cluster.
In a typical map function, it loads, parses, transforms, and filters data. And each reduce task works on the output from the map tasks. Through reduce task, the output is grouped and aggregated to the intermediate data from the map tasks.
The resource management layer of Hadoop is YARN or Yet Another Resource Negotiator. YARN separates resource management and schedules job functions into separate daemons. There is one global ResourceManager in YARN, whereas there are ApplicationMaster per-application. An Application could be a single job or a DAG of jobs. The YARN framework consists of two daemons. ResourceManager and NodeManager. The ResourceManager distributes resources among all the competing applications in the system, whereas, NodeManger performs the monitoring of resources. The same is then reported to ResourceManger.
2 # HBase – trendy big data database
While Hadoop can perform only batch processing, databases like HBase can do store massive amounts of data and random access. This open-source project is a horizontally scalable and distributed column-oriented database built on top of the Hadoop file system.
Features
- It is linearly scalable.
- It provides consistent read and writes.
- It has auto- failover support.
- It provides data replication across clusters.
- It can integrate with Hadoop, both as a source and a destination.
- It has an easy java API for clients.
- It is ideal for write-heavy applications.
- HBase can do fast random access to available data.
3 # Apache Spark
Apache Spark is an open-source framework that provides more advanced solutions. It was initially developed to solve some problems associated with Hadoop. The main difference between Apache Spark and Hadoop are their data retrieval model. In Hadoop vs. Spark battle, Spark resolves the speed issue during data retrieval. In Hadoop, data is stored in the hard drive with each stage of Map-Reduce processing, but Spark performs random access memory.
Spark supports multiple programming languages like Java, Scala, Python, and R. Additionally, with more intervention of cloud in Bigdata field, Spark has an additional advantage as it can run standalone, on a cloud, and Kubernetes. Besides, it supports streaming data, complex analytics, and its own set of SQL libraries; MLib libraries are very adaptive considering present technical fields.
You can find more information in this blog – Do you really need Hadoop to run Spark?
4 # Apache Flink – for all big data types
One of the new data processing technologies – Apache Flink, excels at processing bounded, and unbounded data sets. This distributed processing engine is built for stateful computation of data. Flink can run standalone and integrate with common cluster managers like Hadoop YARN, Apache Mesos, or Kubernetes. It can run stateful streaming applications at any scale.
However, the real applications of Flink are in the below areas –
- Event-driven Applications
- Data Analytics Applications
- Data Pipeline Applications
During data processing, it is easy to enact checkpoints using Flink to preserve progress in any failure. This is Flink’s main feature for event-driven applications like Fraud detection, anomaly detection, rule-based alerting system, social networking, etc.
For data analytics applications, Flink supports both batch processing as well as steaming data. Additionally, Flink has ANSI-compliant SQL interface support that provides the same result for batch data and real-time streaming data.
Flink’s data pipeline application serves a similar purpose as ETL jobs. Data Pipeline requirement is materialized using DataStream API of Apache Flink.
5# Apache Impala
If you are looking for a choice for interactive BI-like workloads, then Apache Impala is the best choice. This is because Impala queries give you the lowest latency across all other options. With Impala, you can query data in real-time from Hadoop HDFS or Apache HBase.
It has a specialized distributed query engine similar to commercial parallel RDBMS. This gives it a magnitude of faster performance. Impala provides an alternative approach to Hadoop data querying with many exciting features like –
- It processes data locally on data nodes. Consequently, it eliminates network bottlenecks.
- It uses a single, open, and unified metadata store.
- No need for costly data format conversion.
- As all data is immediately query-able, there is no chance of delays for ETL.
- Best utilization of all hardware.
- Only a single machine pool is enough to scale.
Conclusion
The software market is a competitive area where we can find no new and exciting products with innovative features. Hopefully, the frameworks mentioned here can help you to navigate it. However, the list is not limited, and many other significant features we can find in other frameworks like Apache Kafka, Apache Hive, Apache TEZ, etc. So, it is better to say there is no single best option among the frameworks. Each one has its pros and cons. Also, some solutions come along with some strict factors for their best performance.