The Most Popular Big Data Frameworks in 2023
Big data refers to the massive volume of information generated by digital devices, social media platforms, and other online sources in people's daily lives. With the help of advanced tools and technologies, big data can be harnessed to uncover hidden patterns, trends, and correlations to improve decision-making, optimize processes, and even predict future events, ultimately enhancing the quality of life for individuals, businesses, and societies as a whole.
With more and more data being generated, it can be difficult for businesses and researchers to gain insights in a timely manner. As such, Big Data frameworks are becoming increasingly important. In this article, we'll look at the most popular big data frameworks – such as Apache Storm, Apache Spark, Presto and others – that are becoming increasingly popular for Big Data analytics.
What Are Big Data Frameworks?
Big data frameworks are tools that make it easier to process big data. They're designed to process big data quickly and efficiently, while also being secure. Big data frameworks are usually open-source, meaning they're free with the option of paying for support if you need it.
Big Data needs frameworks!
Big Data is about collecting, processing, and analyzing petabytes and exabyte scale data sets. Big Data is about the volume, velocity, and variety of data. Big Data is about the ability to process and analyze data at a speed and scale that was previously impossible.
1. Hadoop
Apache Hadoop is an open-source framework for storing and processing large amounts of data. It's written in Java and can be used for batch processing, stream processing, and real-time analytics.
Apache Hadoop hosts a number of applications that enable you to work with large amounts of data on a single machine or on several machines through a network in such a way that the applications are not aware that they are distributed across multiple machines.
One of Hadoop’s key strengths is its ability to efficiently handle vast amounts of data. Built on a distributed computing model, Hadoop breaks down large datasets into smaller chunks, which are then processed in parallel across a cluster of nodes. This approach helps achieve a high level of fault tolerance and faster processing speeds, making it ideal for handling Big Data workloads.
On the downside, Hadoop’s batch-processing nature can hinder real-time data processing and analysis. Hadoop’s learning curve can also be steep for those unfamiliar with Java or similar programming languages. Moreover, setting up and managing Hadoop clusters can be complex, time-consuming, and resource-intensive, posing challenges for organizations with limited resources or expertise in Big Data.
Hadoop has succeeded across various industries, including finance, healthcare, retail, and telecommunications. Its specific use cases span from log analysis and fraud detection to recommendation engines and sentiment analysis.
2. Spark
Apache Spark is a fast and general engine for large-scale data processing. It provides high-level APIs in Java, Scala, Python, and R (a statistical programming language), meaning that any developer can use them. Spark is widely used in production environments to process data from multiple sources, including HDFS (Hadoop Distributed File System) and other file systems, Cassandra databases, Amazon S3 storage service (which offers web services for storing data on the Internet), as well as external web services such as Google's Datastore.
Spark's primary advantage stems from its capacity to process data at remarkable speeds, made possible by its in-memory processing features. This significantly reduces I/O operations, making it well-suited for extensive data analysis tasks. Moreover, Spark offers considerable flexibility, accommodating various data processing tasks, including batch processing, streaming, machine learning, and graph processing through its integrated libraries.
Nonetheless, Spark also exhibits some drawbacks. Its memory-intensive nature can increase expenses for organizations with constrained resources or budgets. Additionally, Spark might not be the optimal choice for applications necessitating real-time data processing since it is designed for micro-batching rather than true real-time processing.
Spark has gained popularity in sectors like finance, healthcare, and telecommunications. For example, financial institutions employ Spark to process large amounts of transactional data for detecting fraudulent activities or evaluating customer credit risk. Healthcare organizations can utilize Spark to examine electronic health records and genomic data, leading to more tailored patient care.
Spark supports two modes for analytics: batch and streaming.
Batch Mode
In batch mode, Spark Streaming reads a large amount of data from a single source and stores the result in memory or disc. After the batch is processed, you can use an API such as SQL or DataFrames to analyze the results. Batch mode is useful when you have to process historical data or when you have to use existing tools like Hive without writing any code.
Streaming Mode
In streaming mode, Spark Streaming continuously reads incoming data in small chunks and feeds them into Spark's Resilient Distributed Datasets (RDDs). You can then apply transformations and actions on these RDDs to produce new results that are outputted as another RDDs object. In streaming mode, you don't need to specify how much data will be read from each source because Spark handles that automatically for you based on your application logic.
3. Hive
Apache Hive is an open-source data warehouse framework that lets users query and manipulates large datasets. It's a data warehouse infrastructure built on top of Hadoop, and it allows users to write SQL queries and use other languages like HiveQL or Pig Latin (a scripting language).
Apache Hive is part of the Hadoop ecosystem, so you need to have an installation of Apache Hadoop before installing Hive.
One of Apache Hive’s major strengths is its ability to handle petabytes of data efficiently by leveraging Hadoop Distributed File System (HDFS) for storage and Apache Tez or MapReduce for processing.
However, despite its numerous benefits, Hive has certain limitations. Its performance can be slower than other big data processing frameworks as it relies heavily on batch processing, making it less suitable for real-time or low-latency applications. Moreover, Hive’s support for iterative algorithms and machine learning is limited compared to other frameworks like Apache Spark.
Hive excels in scenarios where data warehousing and batch processing are crucial such as log analysis, text mining and large-scale data transformations.
4. Elasticsearch
https://www.elastic.co/elasticsearch/
Elasticsearch is a fully managed, open-source, distributed, column-oriented search and analytics engine. Elasticsearch is used for search (elastic search), real-time analytics (Kibana), log storage/analytics/visualization (Logstash), centralized server logging aggregation (Logstash Winlogbeat), and data indexing.
Elasticsearch consulting can be used to analyze big data because it's scalable, fault-tolerant, and provides a distributed architecture that allows you to run multiple nodes on different servers or even cloud instances. It features an HTTP interface with JSON support which makes it easy to integrate with other applications through common APIs like RESTful calls or Java Spring Data JPA annotations on domain classes.
Furthermore, Elasticsearc has an impressive ability to perform real-time distributed search and analytics, enabling businesses to process vast amounts of data in milliseconds.
Despite its many advantages, Elasticsearch requires a solid understanding of its complex architecture, making it challenging for beginners to implement and maintain.
Additionally, Elasticsearch can consume significant resources and may require powerful hardware for optimal performance, potentially increasing infrastructure costs.
Elasticsearch is employed across diverse industries for various use cases, including full-text search, log analysis, and monitoring applications. Companies often use it to power their search engines and provide users with relevant and accurate results. Furthermore, Elasticsearch is a popular choice for analyzing massive log data sets in real-time to identify patterns, anomalies, and trends.
5. MongoDB
MongoDB is a NoSQL database. It stores data in JSON-like documents, meaning that there is no need to define schemas before writing your application. MongoDB is open-source, and it's available both as on-premises software and as a cloud service (MongoDB Atlas).
MongoDB can be used for many purposes: from logging to analytics, and from ETL to machine learning (ML). You can store millions of documents without worrying about performance issues because of its horizontal scaling model and efficient memory management. In addition to its simplicity for software developers who want to focus on building their applications instead of worrying about data model design or tuning the underlying systems, MongoDB offers high availability through replica sets—a cluster architecture where multiple nodes replicate each other's data automagically—or manually set up clusters with automatic failover support when one node fails.
This is because MongoDB’s primary strength lies in its schema-less design, which allows for easy adaptation to changing data structures. This flexibility enables developers to work with heterogeneous data without the need for rigid schemas. Additionally, its horizontal scaling capabilities ensure that it can accommodate increasing data loads, making it an excellent choice for big data applications.
However, while MongoDB excels in many aspects, it may not be the best choice for applications requiring complex transactions and strong consistency. Its eventual consistency model might lead to temporary data inconsistencies.
MongoDB is particularly well-suited for applications that need to store and manage large amounts of unstructured or semi-structured data. Some specific use cases include content management systems, IoT data platforms or real-time analytics.
6. MapReduce
MapReduce is a framework for processing large datasets on a cluster. It is designed to be fault-tolerant and distribute the work across machines.
MapReduce is a batch-oriented framework, which means that it can process huge amounts of data and get results in a short period of time.
It is an algorithm, or group of steps that perform computation on data while taking into account the properties of that particular type of data (in this case, large). It also has several programming models that have been derived from it over time: Hadoop MapReduce (Hadoop), Google MapReduce (GMR), Spark SQL Mapper/Reducer/Aggregator/GroupByKey/Joiners/Cogroups, Giraph GraphX GraphLab Cytus Notebook.
MapReduce’s primary strength lies in its ability to distribute large-scale data processing tasks across multiple nodes, allowing for parallel execution and thus significantly improving performance. This is achieved through a two-step process: the ‘Map’ step, which breaks down the input data into key-value pairs, and the ‘Reduce’ step, which aggregates these pairs to produce the desired output. MapReduce’s fault tolerance and scalability make it a robust solution for big data challenges.
However, MapReduce has its drawbacks. Its batch-processing nature makes it unsuitable for real-time or low-latency applications. Additionally, the framework’s rigid structure can make it difficult for developers to adapt it to more complex data processing tasks.
Despite the drawbacks, MapReduce has found its niche in various big data scenarios such as log analysis, data transformation, large-scale text processing and pattern-based searching.
7. Samza
Samza is a stream processing framework. It uses Apache Kafka as the underlying data store and message bus and runs on YARN. The Samza project is hosted at Apache, which means it's open-source and free to use, modify and redistribute under the Apache License version 2.0.
As an example of how this works in practice: A user who wants to process a stream of messages may write their application using any language they choose (Java or Python are currently supported). That application will run in a container on one or more worker nodes that are part of the Samza cluster. These workers form a pipeline that processes incoming messages from Kafka topics in parallel with other similar pipelines—each message will be received by all workers responsible for handling it before being sent back out again into Kafka somewhere else within the system or even outside it if necessary to keep up with demand.
One of the key strengths of Samsza is its fault-tolerant nature, which allows it to maintain high availability and reliability in complex, distributed environments. Samsza's ability to scale horizontally ensures that it can handle a growing volume of data without performance degradation. Furthermore, its integration with Apache Kafka and YARN makes it an ideal choice for organizations already utilizing these technologies.
Samsza is well-suited for specific use cases, including real-time data processing and analytics, event-driven applications, and data pipeline management. Examples include monitoring and analyzing user activities on e-commerce websites, processing IoT sensor data for smart cities, and managing large-scale log data for system performance analysis.
8. Flink
Flink is a data stream processing framework. It is also a hybrid big data processor. Flink can be used for real-time analytics, ETL, and batch processing.
Flink’s design makes it well suited for stream processing and interactive queries on large datasets. Flink supports both event time and processing time semantics for data streams, which allows it to handle both real-time analytics as well as historical analysis in the same cluster with the same API.
The biggest difference between Spark Streaming and Flink is that Spark Streaming works with unbounded streams of data while Flink works with bounded streams of data. The bounded nature of Flink lets you set time bounds on your streaming dataset so that they only exist within a certain timeframe (e.g., 1 minute). This lets you avoid running into memory issues when dealing with very large amounts of data at any one point in time but still process them quickly enough to keep up with changes in your environment!
Despite its impressive capabilities, Flink does have some drawbacks. It requires considerable resources and expertise to set up and manage, which might pose a challenge for organizations with limited resources. Additionally, while Flink is known for its real-time processing capabilities, it may not be optimal for batch processing workloads.
Flink is particularly well-suited for applications that demand real-time data processing, such as financial transaction analysis, anomaly detection, and event-driven applications in IoT ecosystems. Moreover, its machine learning and graph processing support makes it a versatile choice for data-driven decision-making processes in various industries.
9. Heron
https://heron.incubator.apache.org/
Heron is a distributed stream processing engine that is used to process real-time data. It can be used for building low latency applications like microservices and IoT devices. Heron is written in C++ and it provides a high-level programming model for writing distributed stream processing applications on Apache YARN, Apache Mesos, and Kubernetes by tightly integrating with Kafka or Flume as the underlying messaging layer.
Heron's key strength lies in its ability to provide fault tolerance and excellent performance in processing large-scale data. It is designed to overcome the limitations of its predecessor, Apache Storm, by introducing a new scheduling model and a backpressure mechanism. This allows Heron to maintain high throughput and low latency, making it ideal for organizations dealing with massive data sets.
Heron is well-suited for a variety of real-time big data use cases, including social media sentiment analysis or trend detection, analyzing real-time IoT sensor data for predictive maintenance or anomaly detection, and monitoring and analyzing log data for security or performance insights in large-scale applications.
10. Kudu
Kudu is a columnar storage engine for analytical workloads. Kudu is the new kid on the block, but it’s already stealing the hearts of developers and data scientists with its ability to combine the best of relational databases and NoSQL databases into one package.
Kudu is a distributed database that combines the best of relational databases (strict ACID compliance) with NoSQL databases (scalability and performance). It also comes with a few added perks: it has native support for streaming analytics, so you can use your SQL skills to analyze data streams in real-time; it supports JSON data storage, and it uses columnar storage to improve query performance by storing related values together.
All of this means that Kudu can store and manage massive volumes of data with high write and scan performance while offering strong consistency and fault tolerance, ensuring the reliability and accuracy of the data.
Despite its advantages, Kudu is unsuitable for small or variable-sized datasets, as its performance benefits are best realized with large datasets.
Kudu excels in use cases that require fast analytics and real-time data processing, such as time-series data analysis, machine data analytics, event logging, and online analytical processing (OLAP). It is especially valuable for finance, telecommunications, and IoT industries, where rapid insights from large datasets are critical for effective decision-making.
11. Presto
Presto is a distributed SQL query engine for running interactive analytic queries against Apache Hadoop data. It’s an open-source project that supports standard ANSI SQL as well as Presto-specific functionality such as window functions and recursive queries (to name a few).
Presto was developed at Facebook, where its creators recognized the drawbacks of Hadoop MapReduce in the context of big data analytics: it was slow to execute, not suitable for interactive querying, and lacked support for complex analytical operations like JOINs. The result was a new way to work with massive amounts of data—allowing users to run complex queries on datasets in milliseconds rather than hours or days—and it's this speed that makes Presto so attractive today!
Presto is able to process massive volumes of data across multiple data sources, such as Hadoop, S3, and various databases, with exceptional query performance. Its in-memory, pipelined execution model and support for a wide range of data formats and connectors ensure that users can query and analyze data quickly and easily.
However, Presto's main weakness is its lack of support for real-time data processing, as it is designed primarily for batch query processing. Additionally, while its performance is impressive, the memory-intensive nature of the framework may lead to high resource consumption, which could be a concern for organizations with limited infrastructure.
Presto is well-suited for use cases that require interactive ad-hoc querying of large datasets, such as business intelligence and data analytics applications. It is also ideal for scenarios where accessing and analyzing data across multiple sources is crucial, such as data federation and multi-source data integration tasks.
Big Data Frameworks are complex
Big Data frameworks are complex. They're designed to process large amounts of data, and they have many different applications.
Big Data frameworks can be used for many different purposes, such as:
- Business intelligence (BI) and analytics
- Machine learning and artificial intelligence (AI)
- Streaming data processing or real-time analytics
Streaming Data Framework
The streaming data framework is used to process data in real-time. It is a powerful tool for aiding the analysis of large volumes of information, as it allows users to process data as it arrives.
Data streams are unstructured and semi-structured, so there must be a way of dealing with this kind of data. Stream processing is designed specifically to deal with real-time problems such as monitoring applications or analyzing sensor data. A stream processor can process streams that may be very large in size while maintaining low latency (the time it takes before the result appears).
This framework can be used for a wide range of different tasks and applications, including:
Real-time analytics and reporting systems
Data Analytics Framework is a framework that allows you to integrate different types of data sources and data processing frameworks to build a data analytics application.
It allows you to build a data analytics application with a single code base, which is easy to deploy, maintain, and scale. The Data Analytics Framework provides an easy-to-use interface for creating your own custom adapters, which can be used in any applications built with the framework. It also integrates common enterprise applications such as Spark SQL and Hive using point-to-point integration between these systems.
The Data Analytics Framework provides advanced features that enable users/developers/administrators to work together more efficiently when building advanced analytics applications:
- Multi-tenancy: Supports multi-tenancy for all tenants on one instance or cluster of servers
- Multi-instance support: Provides robust load balancing capabilities across multiple instances
Machine learning algorithms for real-time decision making
Machine learning is a way for computers to get better at performing tasks by finding patterns in data. It's used in everything from search engines and image recognition, to credit card fraud detection and medical research.
Machine learning algorithms look at the behavior of entities—people, places, things—and make predictions about how those entities will behave in the future based on their past behavior. These algorithms are especially useful when you have large amounts of data from virtual deal room that traditional methods can't handle effectively because they don't scale well with large datasets (think billions).
Enhanced Data Streaming Processing (EDS)
Streaming data processing is the process of analyzing streaming data. Streaming data is a continuous flow of events, such as clicks on an internet site or air temperature measured at a weather station. In both cases, the events happen in real-time and there is no way to store the incoming stream before processing it. To make sense of this stream and find useful information, you need to process it instantly. Data streaming is a way to do that: instead of waiting until all events come in and then processing them, you break up your task into smaller chunks (streams), each one processed on its own instance as soon as possible. This allows for more parallelization than batch processing would allow for - which means more efficiency when you want answers quickly!
EDS Processing with Machine Learning
Machine learning algorithms are used in big data frameworks to process, analyze, and my large amounts of data. There are many machine learning algorithms to choose from, each with its own purpose and use case.
The most common ones include:
- k-means clustering
- regression analysis
- decision trees (binary or multiway)
Where to learn about Big Data
If one is interested in a career in big data, there are many resources available to help one learn more about this field. Some popular online courses include “Introduction to Big Data” on Coursera and “The Big Data Developer Course” on Udemy.
Additionally, Big Data books on Amazon such as “Getting a Big Data Job For Dummies” and “The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition” will give more insight into Big Data. Furthermore, online Big Data resources include the Enterprise Big Data Framework Alliance, which provides certifications and training in Big Data for aspiring Big Data practitioners.
Conclusion
Big data is the emerging area of focus that takes the notion of huge information sets and crunched them with hardware architecture of high-speed parallel processors, storage hardware and software, APIs, and open-source software stacks. it’s an exciting time to be a data scientist. Not only are there more tools than ever before in the Big Data ecosystem, but they are also becoming more robust, easier to use, and cheaper to run. This means that companies can get more value out of their data without having to spend as much money on infrastructure.
