Big Data Frameworks for Data Science

By | April 29, 2021
Big Data Frameworks

This list contains the 10 best big data frameworks to use in 2021. However, please note that this is not a universal list i.e. your views might differ from ours. Nonetheless, we would love to know about your best picks in the comments section at the end of the article. Thanks in advance!

Big data refers to huge datasets that usually keep growing with each passing day. For example, the number of Facebook users keeps growing every day, and each user’s data also grows as they browse through Facebook.

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Such data can be structured as well as unstructured. The data is big in size and, thus, bigger in complexity and speed, i.e., it is fast and complex. Big data is thus identified by the 3Vs, i.e., Volume, Variety, and Velocity.

Big data helps us analyze data and perform various operations on it to optimize cost and time. When we use this big data with robust frameworks, it becomes easier to find the exact (solution to the) problem or issue in real-time.

The aforementioned also allows us to make dynamic offers to users, detect real-time fraud, and much more. Big data frameworks have the following advantages:

  • Provide a structure and common reference to organizations to explore the full potential of big data.
  • Improve the approach to collecting, storing, managing, and sharing data, and use data effectively for business purposes.
  • Perform advanced analytics to get better insights and make intelligent data-driven decisions.
  • Tap data from various sources and capture different types of data to find the best – and most useful – insights.
  • Faster and affordable. Can reuse common pieces of code and has great community support.
  • Facilitate advanced analytics through visualization and predictive analysis.

 

What does the Big Data Frameworks Include? The Big Data Framework Structure

The big data framework consists of 6 important elements:

1. Big Data Strategy

The strategy includes analyzing the most likely areas to return more business value and improve sales. Out of the big chunk of data, only the relevant data can be picked up if the strategy is clearly defined, making the analysis easier.

2. Big Data Architecture

There are many architectures to store and process huge datasets. The architecture to be chosen depends on the project and business needs. It includes considering the technical capabilities of a framework for the storage and processing of big data.

3. Big Data Functions

Functions involve assigning roles and responsibilities to an organization’s resources so that the best results can be obtained. It covers the non-technical aspects of Big Data.

4. Big Data Algorithms

Working with statistics and algorithms forms the core of big data analysis, processing, and automation of tasks. Algorithms cover the technical aspects of handling big data, like getting insights and knowledge from the data.

5. Big Data Processes

These processes give a structure to the project and make it easy to track goals on a day-to-day basis. The process helps an organization focus on the business while following the best practices and measures.

6. (Surprisingly) AI or Artificial Intelligence

Since AI can learn from Big Data, it is the next logical step of a big data framework. The framework brings business benefits by taking a functional view of AI.

 

Top 10 Big Data Frameworks

There are many big data frameworks available in the market, out of which the following are the most popular and can yield, in our humble opinion, the best results for your business.

These are among the top big data frameworks of 2021. It all started with Apache Hadoop, which revolutionized the storage and processing of big data. Despite emerging names, Hadoop remains to be popular among them all.

So, let us begin discussing the 10 popular big data frameworks, starting with Apache Hadoop:

1. Hadoop

Hadoop is a Java-based open-source big data framework that provides batch processing and data storage services. It has a giant architecture consisting of many layers, like HDFS and YARN for data processing.

Storage happens across various hardware machines arranged as clusters. Hadoop provides a distributed environment with the following main components:

  • HDFS (Hadoop Distributed File System), the hardware layer, stores data in the Hadoop cluster, including replication and storage activities across all the data clusters.
  • YARN (Yet Another Resource Negotiator) is responsible for job scheduling and resource management.
  • MapReduce is the software layer that works as the batch processing engine and processes huge data in a cluster.

Hadoop is fast and can store petabytes of data. The performance gets better as the data storage space increases.  Many big companies like Alibaba, Amazon, eBay, and Facebook use HDFS to store data and integrate with many popular big data analytics frameworks.

Pros:

  • Cost-effective, reliable
  • Compatible with most popular big data technologies
  • High scalability
  • Multiple language support
  • Fault-tolerant
  • Good failure handling mechanism

Cons:

  • Not suitable for real-time processing
  • Has many processing overheads as it does not perform in-memory computations
  • Not very secure

 

2. Apache Spark

Spark is a batch processing framework with enhanced data streaming processing. It facilitates in-memory computations, making the same superfast. The big data framework integrates with Hadoop and can act as a standalone cluster tool.

Spark is used by companies like Amazon, Hitachi solutions, Baidu, and Nokia. Spark supports 4 languages, namely Python, R, Java, and Scala. It has 5 main components:

  • HDFS and HBase, which form the first layer of storage systems.
  • YARN manages the resources.
  • Core engine that performs task management, memory management and defines RDD (Resilient Distributed Datasets) API, which is responsible for distributing data across the nodes for parallel processing.
  • Utilities containing Spark SQL to execute SQL queries for stream processing, GraphX to process graph data, and MLLib for machine learning algorithms.
  • API for integration with programming languages like Java and Python.

Pros:

  • Extremely fast parallel processing
  • Scalable
  • Fault-tolerant
  • Integration support for Hadoop
  • Support for advanced analytics and AI implementations
  • A smaller number of I/O operations to disk

Cons:

  • Set up and implementation takes time and is a bit complex
  • Supports only a few languages

Learn Data Science with Spark.

 

3. MapReduce

MapReduce is a big data search engine and part of the Hadoop framework. Initially, it was just an algorithm to process huge volumes of data parallelly. Now, it is more than just that and works in 3 stages:

  • Map: This stage handles the pre-processing and filtration of data.
  • Shuffle: Shuffles (sorts) the data as per the output key, which is generated by the map function.
  • Reduce: Reduces the data based on the function set by the user and produces the final output.

Although many new technologies have come, MapReduce is popular and much used because it is resilient, stable, fast, scalable, and based on a simple model. Further, it is secure and fault-tolerant for failures like crashes and omissions.

Pros:

  • Handles data-intensive applications well
  • Simple to learn
  • Flexible
  • Best suited for batch processing

Cons:

  • Requires a large amount of memory
  • Needs a pipeline of multiple jobs
  • Real-time processing is not possible

 

4. Apache Hive

Apache Hive

Facebook designed the Apache Hive as an ETL and data warehousing tool. It is built on top of the HDFS platform of the Hadoop ecosystem. Hive consists of 3 components, namely clients, services and storage, and computing.

Apache Hive has its own declarative language for querying, namely HiveQL, which is highly suitable for data-intensive jobs. Companies like JP Morgan, Facebook, Accenture, and PayPal use Hive. The Hive engine converts queries and requests into MapReduce task chains using the following:

  • Parser: Takes in the SQL request and parses and sorts them.
  • Optimizer: Optimizes the sorted requests.
  • Executor: Sends the optimized tasks to the MapReduce framework.

Pros:

  • Runs queries very fast
  • Even joins can be written and run quickly and easily
  • Multiple users can query the data (using HiveQL)
  • Easy to learn

Cons:

  • Data has to be structured for processing
  • Not suitable for processing online transactions (OLTP) but suitable only for online analytical processing (OLAP)
  • HiveQL doesn’t support updates and deletes

 

5. Flink

FlinkBased on the Kappa architecture, Flink is an open-source single-stream processing engine. It has one processor which treats the input as a stream, and the streaming engine in real-time processes the data. Batch processing is a special case of streaming. Flink architecture has the following components:

  • Client: Takes the program, builds a job dataflow graph, and passes it to the job manager. The client is also responsible for retrieving job results.
  • Job Manager: Creates the execution graph based on the dataflow graph received from the client. Then, assigns and supervises the jobs to task managers in the cluster.
  • Task Manager: Executes tasks assigned by the JobManager. Multiple task managers perform their specified tasks parallelly.
  • Program: The code that is run on the Flink cluster.

Flink APIs are available for Java, Python, and Scala. It also provides utility methods for common operations, event processing, machine learning, and graph processing. Apache Flink processes data in the blink of an eye. It is highly scalable and scales thousands of nodes of a cluster.

Pros:

  • High-speed processing
  • Easy to learn and use APIs
  • Better testing capabilities
  • Unified programming
  • Work on file systems other than the HDFS

Cons:

  • APIs are still raw and can be enhanced
  • Memory management can be an issue for longer running pipelines
  • Limited fault tolerance compared to competitors

 

6. Samza

SamzaThrough Samza, you can build stateful applications that can process real-time data from various sources. It was built to solve the batch processing latency (large turn-around time) problem.

Some of the input sources are Kafka, HDFS, Kinesis, and Eventhubs. The unique feature of Samza is that it is horizontally scalable. It also has rich APIs, like Streams DSL, Samza SQL, or Apache Beam APIs.

You can process both batch and streaming data using the same code (write once, run anywhere!). LinkedIn created and used Samza architecture, which consists of the following components:

  • Streaming layer: Provides partitioned streams that are durable and can be replicated.
  • Execution layer: Schedules and coordinates tasks across machines.
  • Processing layer: Processes and applies transformations to the input stream.

The streaming layer (Kafka, Hadoop, etc.) and the execution layer (YARN, Mesos) are pluggable components.

Pros:

  • Makes full use of the Kafka architecture for fault tolerance, state storage, and buffering
  • More reliable as there is better isolation between tasks (as Samza uses separate JVM for each stream processor)

Cons:

  • Supports only JVM languages
  • The use of a separate JVM can result in memory overhead
  • Doesn’t support low latency
  • Depends on the Hadoop cluster for resource negotiation

 

7. Storm

Apache Storm

Storm works with a huge real-time data flow. It was built to handle low latency and is highly scalable. Storm can recover faster after a downtime. In fact, it was Twitter’s first big data framework, after which it has also been adopted by giants like Yahoo, Yelp, and Alibaba.

Storm supports Java, Python, Ruby, and Fancy. The Storm architecture is based on the master-slave concept and consists of 2 nodes:

  • Master node: Allocates tasks and monitors machine/cluster failures.
  • Worker node: Also called supervisor nodes. Worker nodes are responsible for task completion.

The big data framework is platform-independent and fault-tolerant. Although it is said to be stateless, Storm does store its state using Apache ZooKeeper. It has an advanced topology, namely, Trident topology, that maintains state.

Pros:

  • Open-source
  • Flexible
  • Performance is always high as resources can be linearly added under high load (scalable)
  • High-speed real-time stream processing
  • Guaranteed data processing

Cons:

  • Complex implementation
  • Debugging is not easy
  • Not so easy to learn

 

8. Impala

In C++ and Java, Impala is an open-source massive parallel processing query engine that processes enormous volumes of data in a single Hadoop cluster.

Just like Hive has its own query language, Impala has one too! It has low latency and high performance and it gives a near RDBMS experience in terms of performance and usability. Impala is like the best of both worlds: the performance and support of SQL like query language and Hadoop’s flexibility and scalability.

It is based on daemon processes that monitor query execution, making it faster (than Hive). Impala supports in-memory data processing. It is decoupled from its storage engine and has 3 components:

  • Impala daemon (impalad): Runs on all the nodes where Impala is installed. Once a query is received and accepted, impalad reads and writes it to data files and distributes the queries to the nodes in that cluster. The results are then received by the coordinating node that initially took the query.
  • statestore: Checks the health of each Impala daemon and updates the same to the other daemons.
  • metastore & metadata: Metastore is a centralized database where table and column definitions and information are stored. Impala nodes cache metadata locally so that they can be retrieved faster.

Pros:

  • If you know SQL, you can quickly work with Impala
  • Uses the Parquet file format, which is optimized for large-scale queries, like in a real-time use case
  • Uses EDA and data discovery to make data loading and reorganizing faster
  • No data movement as processing occurs where data resides

Cons:

  • No support for indexing, triggers, and transactions
  • Tables have to be always refreshed whenever new data is added to the HDFS
  • Can only read text files and not custom binary files

 

9. Presto

prestodbPresto is an open-source distributed SQL tool suited for smaller datasets (Tb). It provides fast analytics and supports non-relational sources, like HDFS, Cassandra, and MongoDB and relational sources, like MSSQL, Redshift, and MySQL.

The big data framework has a memory-based architecture where query execution runs in parallel, and results are often obtained in seconds. Facebook, Airbnb, Netflix,  Nasdaq, and many more giant firms use Presto as their query engine. Presto runs on Hadoop and uses a similar architecture to that of Massively Parallel Processing, having:

  • Coordinator nodes: Users submit their queries to this node, which then uses a custom query and the engine to distribute and schedule queries across the worker nodes.
  • Worker nodes: Execute the assigned queries parallel, and thus, saves time.

Pros:

  • User-friendly
  • Query execution time is extremely low
  • Minimal query degradation when there is a high workload
  • Easy to add images and links

Cons:

  • Reliability issues in terms of results

 

10. HBase

HBase can store humongous amounts of data and process and access it randomly. Built on top of the Hadoop file system, HBase is linearly scalable, and uses distributed column-oriented database architecture. HBase also provides data replication across clusters and automatic fail-over support.

The big data framework also has a Java API for clients. Tables are split into regions taken care of by the region servers. Regions are further vertically split into stores. Stores are saved as files in the Hadoop file system. There are 3 main components in HBase:

  • MasterServer: Maintains the state of the cluster, handles load balancing, and responsible for the creation of tables and columns.
  • RegionServer: Handles data-related operations, determines the size of each region, and handles read and write requests for all the regions under it.
  • Client library: Provides methods for the client for communication.

Pros:

  • Uses hash tables internally to provide random access, and stores data in indexed files thus, enabling faster lookup
  • No fixed schema thus, flexible
  • Auto-sharding
  • Provides row-level atomicity
  • Can be integrated with Hive (if one has to work with SQL)

Cons:

  • No support for transactions, if the master fails, the cluster goes down (single failure point)
  • No built-in permissions or authentications
  • Joins and normalization processes are difficult
  • More hardware requirements make it a bit costly

 

Conclusion

That completes our list of the 10 best big data frameworks. However, there are many other – deserving – big data frameworks that we have not covered in this article but need a mention:

  • Heron,
  • Kudu,
  • Open Refine,
  • Kaggle,
  • Cloudera, and
  • Pentaho.

Each big data framework has been developed with some unique features and purpose, and we cannot say that one big data framework fits all the projects. That is because every project has different requirements and hence, needs the framework best suited for that particular project.

For example, if your project needs batch processing, Spark is a great choice. For data-intensive jobs, Hive is much suitable and is easier to learn too. Storm and Flink are both great choices for dealing with real-time streaming requirements.

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