The Best Big Data Platforms: Everything You Need to Know

With the incredible surge in data generation, big data has emerged as a pivotal force driving innovation and growth for businesses globally. As organizations collect more data than ever, they need the right tools to manage and analyze data effectively. Big data platforms provide the infrastructure and tools businesses need to store, process, and analyze extensive and complex datasets.

Choosing the right big data platform is a strategic decision that can significantly impact an organization's ability to extract valuable insights and make data-driven decisions. To help you navigate this complex choice of big data platforms, we have curated a list of the best big data platforms and solutions shaping the future of big data.

So, what exactly is a big data platform? What are its features, workflow, and factors to consider when choosing a big data platform? In this blog, we answer these questions and offer a comprehensive overview of the best big data platforms you can choose from.

Let’s get started!

In the digital transformation era, the sheer volume of data generated has necessitated the development of specialized platforms to handle and analyze this massive flow of information. Big data platforms are comprehensive frameworks that enable organizations to store, process, and analyze vast amounts of structured and unstructured data.

At their core, big data platforms are comprehensive ecosystems of tools, technologies, and infrastructure designed to handle the three V's of big data: volume, velocity, and variety. These platforms empower businesses to identify trends and optimize operations by leveraging distributed computing, parallel processing, and advanced analytics techniques. From data ingestion and storage to data processing and visualization, big data analytics platforms offer a comprehensive solution for managing and harnessing the power of data in the modern age.

Big data platforms offer several features - from data sourcing to advanced analytics, helping businesses utilize data to achieve their business objectives. Some prominent features offered by big data platforms include:

Data storage and management is a fundamental feature of big data platforms. These platforms provide robust and scalable storage solutions for handling large volumes of structured and unstructured data. They offer various storage options, such as distributed file systems, NoSQL databases, and data lakes, allowing organizations to store and organize data efficiently.

One key advantage of big data platforms is the support for distributed file systems like Hadoop Distributed File System (HDFS) and cloud-based storage options, which enable seamless data storage across various environments. With advanced data management capabilities, these platforms facilitate data integration, cleansing, and transformation, ensuring the data is readily accessible for analysis and decision-making.

Distributed processing is a crucial feature of big data platforms that enables processing large volumes of data across multiple nodes or servers in a distributed computing environment. This feature allows big data platforms to scale horizontally, meaning they can be easily expanded to handle more data by adding more nodes.

This approach enables parallel or simultaneous processing, significantly reducing the time required for data analysis. By distributing the workload across multiple nodes, big data platforms handle massive data sets that would be impractical on a single machine. Distributed processing is essential for achieving scalability and high performance in big data environments.

Fault tolerance refers to the ability of a system to continue functioning even in the event of software or hardware failures. The risk of failures is significantly higher in big data, where massive data is processed and analyzed. A fault-tolerant big data platform ensures that data processing and analytics operations can continue seamlessly, even if individual components or nodes within the system fail.

Fault tolerance is achieved through various techniques such as data replication, distributed computing, and automatic failover mechanisms. In the event of a hardware failure or software glitch, the system can seamlessly switch to backup resources, preventing data loss and minimizing disruptions. This feature ensures continuous data availability and uninterrupted processing crucial for mission-critical applications and real-time analytics.

The big data analysis platforms offer robust tools and algorithms that can process large volumes of data in real-time or near real-time. Big data platforms support various analytical techniques - from descriptive analytics to predictive and prescriptive analytics for processing complex business data.

Additionally, big data platforms offer advanced visualization capabilities, allowing users to create interactive dashboards, charts, and graphs to convey insights in a visually appealing and easily understandable manner. These capabilities enhance data comprehension and facilitate effective communication across different teams and stakeholders.

Big data platforms follow a structured process to ensure companies can harness data to make informed decisions. This process involves the following steps:

Data collection is the initial step in the operation of big data platforms. It systematically gathers data from various sources such as databases, social media, sensors, and other sources. The data is collected using various methods such as web scraping, data feeds, APIs, and data integration tools. The collected data is then stored in a centralized repository, often a data lake or a data warehouse, where it can be easily accessed and processed for further analysis.

Once the data is collected, it must be stored for efficient retrieval and processing. Big data platforms typically utilize distributed storage systems that can handle large volumes of data. These systems include Hadoop Distributed File System (HDFS), Google Cloud Storage, or Amazon S3. This distributed storage architecture ensures high availability, fault tolerance, and scalability.

Once the data is collected, it must be processed to extract valuable insights. This process involves various operations such as cleaning, transforming, and aggregating the data. The parallel processing capabilities of big data platforms, such as Apache Hadoop and Apache Spark, enable rapid computations and complex data transformations.

Data analysis involves examining and interpreting large volumes of data to extract meaningful insights and patterns. The analysis process includes using machine learning algorithms, data mining techniques, or visualization tools to better understand the information. The analysis results can then be used to make data-driven decisions, optimize processes, identify opportunities, or solve complex problems.

This stage ensures accuracy, consistency, integrity, relevance, and data security. The prominent techniques to implement data quality and governance include data quality management, lineage tracking, and cataloging. By implementing robust data quality assurance measures, organizations can have confidence in the data they use for decision-making.

Data management is a crucial aspect of big data platforms. It involves organizing, storing, and retrieving large volumes of data. Platforms employ various techniques such as data backup, recovery, and archiving to manage data effectively. These techniques help implement fault tolerance and ensure optimized data retrieval for all use cases.

Several big data platforms offer comprehensive features and solutions for businesses to manage and analyze complex datasets. The most prominent big data platforms used by companies include the following:

Apache Hadoop is one of the industry's most widely used big data platforms. It is an open-source framework that enables distributed processing for massive datasets throughout clusters. Hadoop provides a scalable and cost-effective solution for storing, processing, and analyzing massive amounts of structured and unstructured data.

One of the key features of Hadoop is its distributed file system, known as Hadoop Distributed File System (HDFS). HDFS enables data to be stored across multiple machines, providing fault tolerance and high availability. This feature allows businesses to store and process data at a previously unattainable scale. Hadoop also includes a powerful processing engine called MapReduce, which allows for parallel data processing across the cluster. The prominent companies that use Apache Hadoop are:

• Yahoo
• Facebook
• Twitter

Apache Spark is a unified analytics engine for batch processing, streaming data, machine learning, and graph processing. It is one of the most popular big data platforms used by companies. One of the key benefits that Apache Spark offers is speed. It is designed to perform data processing tasks in-memory and achieve significantly faster processing times than traditional disk-based systems.

Spark also supports various programming languages, including Java, Scala, Python, and R, making it accessible to a wide range of developers. Hadoop offers a rich set of libraries and tools, such as Spark SQL for querying structured data, MLlib for machine learning, and GraphX for graph processing. Spark integrates well with other big data technologies, such as Hadoop, allowing companies to leverage their existing infrastructure. The prominent companies that use Apache Spark include:

• Netflix
• Uber
• Airbnb

Google Cloud BigQuery is a top-rated big data platform that provides a fully managed and serverless data warehouse solution. It offers a robust and scalable infrastructure for storing, querying, and analyzing massive datasets. BigQuery is designed to handle petabytes of data and allows users to run SQL queries on large datasets with impressive speed and efficiency.

BigQuery supports multiple data formats and integrates seamlessly with other Google Cloud services, such as Google Cloud Storage and Google Data Studio. BigQuery's unique architecture enables automatic scaling, ensuring users can process data quickly without worrying about infrastructure management. BigQuery offers a standard SQL interface for querying data, built-in machine learning algorithms for predictive analytics, and geospatial analysis capabilities. The prominent companies that use Google Cloud BigQuery are:

• Spotify
• Walmart
• The New York Times

Amazon EMR is a widely used big data platform from Amazon Web Services (AWS). It offers a scalable and cost-effective solution for processing and analyzing large datasets using popular open-source frameworks such as Apache Hadoop, Apache Spark, and Apache Hive. EMR allows users to quickly provision and manage clusters of virtual servers, known as instances, to process data in parallel.

EMR integrates seamlessly with other AWS services, such as Amazon S3 for data storage and Amazon Redshift for data warehousing, enabling a comprehensive big data ecosystem. Additionally, EMR supports various data processing frameworks and tools, making it suitable for a wide range of use cases, including data transformation, machine learning, log analysis, and real-time analytics. The prominent companies that use Amazon EMR are:

• Expedia
• Lyft
• Pfizer

Microsoft Azure HDInsight is a leading big data platform offered by Microsoft Azure. It provides a fully managed cloud service for processing and analyzing large datasets using popular open-source frameworks such as Apache Hadoop, Apache Spark, Apache Hive, and Apache HBase. HDInsight offers a scalable and reliable infrastructure that allows users to easily deploy and manage clusters.

HDInsight integrates seamlessly with other Azure services, such as Azure Data Lake Storage and Azure Synapse Analytics, offering a comprehensive ecosystem of Microsoft Azure services. HDInsight supports various programming languages, including Java, Python, and R, making it accessible to a wide range of users. The prominent companies that use Microsoft Azure HDInsight are:

• Starbucks
• Boeing
• T-Mobile

Cloudera is a leading big data platform that offers a comprehensive suite of tools and services designed to help organizations effectively manage and analyze large volumes of data. Cloudera's platform is built on Apache Hadoop, an open-source framework for distributed storage and processing of big data. Cloudera is a hybrid data platform deployed across on-premise, cloud, and edge environments.

Cloudera offers a unified platform that integrates various components such as Hadoop Distributed File System (HDFS), Apache Spark, and Apache Hive, enabling users to perform various data processing and analytics tasks. Cloudera also provides machine learning and advanced analytics tools, allowing businesses to gain deeper insights from their data. The prominent companies that use Cloudera are:

• Dell
• Nissan Motor
• Comcast

IBM InfoSphere BigInsights is a powerful big data platform that offers a range of tools to manage and analyze large volumes of structured as well as unstructured data in a reliable manner. IBM InfoSphere BigInsights can handle massive data, making it suitable for enterprises dealing with complex datasets. It provides a comprehensive set of features for data management, data warehousing, data analytics, machine learning, and more.

IBM InfoSphere BigInsights provides a user-friendly interface and intuitive data exploration and visualization tools. The platform also offers robust security and governance features, ensuring data privacy and compliance with regulatory requirements. BigInsights is built on top of Apache Hadoop and Apache Spark, and it integrates with other IBM products and services, such as IBM DB2, IBM SPSS Modeler, and IBM Watson Analytics. This integration makes it a good choice for businesses already using the IBM product/services ecosystem. The prominent companies that use IBM Infosphere BigInsights are:

• Lenovo
• DBS Bank
• General Motors

Databricks is a prominent big data platform built on Apache Spark. Databricks simplifies the process of building and deploying big data applications by providing a scalable and fully managed infrastructure. It allows users to process large datasets in real-time, perform complex analytics, and build machine learning models using Spark's powerful capabilities.

Databricks provides an interactive workspace where users can write code, visualize data, and collaborate on projects. It also integrates with popular data sources and tools, making it easy to ingest and process data from various sources. With its auto-scaling capabilities, Databricks ensures that users have the resources to handle their workloads efficiently. Its automated infrastructure management and scaling capabilities make it a reliable choice for handling large datasets and complex workloads. The prominent companies that use Databricks are:

• Nvidia Corporation
• Johnson & Johnson
• Salesforce

Multiple big data platforms offer comprehensive features, integrations, and advanced analytical capabilities. However, choosing the right platform depends on your business needs, resources, and ecosystem. Some factors you need to consider before choosing a big data platform are as follows:

Scalability is a crucial factor to consider when choosing a big data platform. As your data grows, the platform should be able to handle the increasing volume, velocity, and variety of data without compromising performance. A scalable platform allows you to expand your data infrastructure seamlessly as your business needs evolve. The data platform should support horizontal scaling, enabling you to add more servers or nodes to distribute the workload and handle larger datasets.

Performance is another critical factory you must prioritize when considering big data platforms. The chosen big data platform should have excellent data processing speeds, efficient scaling, high fault tolerance, and minimal disruptions. The platform should be able to handle your specific workloads effectively, whether it involves batch processing, real-time analytics, or machine learning tasks, without compromising performance. You must look for features such as parallel processing and distributed computing to ensure optimized performance.

Data security and compliance are paramount, especially in modern times when there is an increased risk of breaches and cyber-attacks. The chosen platform should offer robust security features, including data encryption, access controls, and authentication mechanisms to safeguard sensitive information. Based on your requirements, you must also verify whether the big data platform complies with industry regulations and standards, such as GDPR or HIPAA. A robust security framework is vital to maintain data integrity, protect customer privacy, and avoid legal and regulatory issues.

The big data platform you choose must have a highly intuitive user interface and tools to help users navigate different functions and perform business-specific tasks without extensive technical expertise. A platform having a steep learning curve can hinder adoption and productivity, leading to suboptimal performance and results. The data platform must also provide comprehensive documentation, resources, and tutorials to help users easily harness all the capabilities and features offered by the platform.

The big data platform must seamlessly integrate with your existing ecosystem, including databases and applications. This integration ensures seamless data processing and eliminates the need for complex data migration processes. You must specifically look for platforms that are compatible with various data sources and provide integration with prominent databases, cloud services, and APIs. You must also consider platform compatibility with your preferred programming languages and technologies. A big data platform offering extensive integration helps with the seamless execution of various data-related tasks.

Big data platforms have become essential for modern enterprises harnessing data for making well-informed decisions. These platforms offer cutting-edge capabilities - from data processing to advanced analytics, helping businesses draw critical insights from large data streams. The choice of the big data platform significantly impacts business strategy and growth. Hence, it is vital to conduct a comprehensive analysis before choosing a compatible big data platform for your business needs.

Turing Big Data Services offers end-to-end solutions, helping businesses tap into diverse datasets to drive meaningful insights. We utilize modern big data platforms and innovative technologies to deliver maximum value for your business. Our expertise and strategic guidance can transform your data processes and infrastructure while ensuring maximum data security and compliance.

Talk to an expert today!