Introduction to Big Data Platforms

The phrase “big data” can be traced back to Silicon Valley lunch-table conversations and pitch meetings in the 1990s[1]. It’s a relative term depending on who is discussing it, but one point remains constant: The 21st century has witnessed the greatest explosion of data in history. And that’s why big data platforms and big data consulting became indispensable.

Up until 2003, the total volume of data recorded was 5 exabytes[2]. In the year 2011 alone, the amount of data recorded was 1.8 zettabytes, which is about 1000x more. Moving forward, it is projected that mankind will produce 463 exabytes of data every day worldwide by 2025. That’s equal to 212,765,957 DVDs each day[3]! Judging from this perspective, we can conclude that the volume of big data produced worldwide is bound to grow tremendously in the future.

In this post, we look at the role of big data platforms in storing and processing huge data sets. But first, let’s give a brief description of big data.

You can listen to the audio version of this article here:

What is big data?

Big data is a term used to describe data of great variety, huge volumes, and even more velocity. Apart from the significant volume, big data is also complex such that none of the conventional data management tools can effectively store or process it. The data can be structured or unstructured.

Examples of big data include:

• Mobile phone details
• Social media content
• Health records
• Transactional data
• Web searches
• Financial documents
• Weather information

Big data can be generated by users (emails, images, transactional data, etc.), or machines (IoT, ML algorithms, etc.). And depending on the owner, the data can be made commercially available to the public through API or FTP. In some instances, it may require a subscription for you to be granted access to it.

Read more about: Big data architecture: Definition, processes, and best practices

What is a big data platform?

The constant stream of information from various sources is becoming more intense[4], especially with the advance in technology. And this is where big data platforms come in to store and analyze the ever-increasing mass of information.

A big data platform is an integrated computing solution that combines numerous software systems, tools, and hardware for big data management. It is a one-stop architecture that solves all the data needs of a business regardless of the volume and size of the data at hand. Due to their efficiency in data management, enterprises are increasingly adopting big data platforms to gather tons of data and convert them into structured, actionable business insights[5].

Currently, the marketplace is flooded with numerous Open source and commercially available big data platforms. They boast different features and capabilities for use in a big data environment.

Characteristics of a big data platform

Any good big data platform should have the following important features:

• • Ability to accommodate new applications and tools depending on the evolving business needs
  • Support several data formats
  • Ability to accommodate large volumes of streaming or at-rest data
  • Have a wide variety of conversion tools to transform data to different preferred formats
  • Capacity to accommodate data at any speed
  • Provide the tools for scouring the data through massive data sets
  • Support linear scaling
  • The ability for quick deployment
  • Have the tools for data analysis and reporting requirements

Big Data Platforms vs. Data Lake vs. Data Warehouse

Big Data, at its core, refers to technologies that handle large volumes of data too complex to be processed by traditional databases. However, it is a very broad term, functioning as an umbrella term for more specific solutions such as Data Lake and Data Warehouse.

What is a Data Lake?

Data Lake is a scalable storage repository that not only holds large volumes of raw data in its native format but also enables organizations to prepare them for further usage.

That means data coming to Data Lake doesn’t have to be collected with a specific purpose from the beginning, it can be defined later. Without it, data can be loaded faster since they do not need to undergo an initial transformation process.

In Data Lakes, data is gathered in its native formats, which provides more opportunities for exploration, analysis, and further operations, as all data requirements can be tailored on a case-by-case basis, then – once the schema has been developed – it can be kept for future use or discarded.

Read more about Data Lake architecture

What is a Data Warehouse?

Compared to Data Lakes, it can be said that Data Warehouses represent a more traditional and restrictive approach.

Data Warehouse is a scalable storage data repository holding large volumes of raw data, but its environment is far more structured than in Data Lake. Data collected in Data Warehouse are already pre-processed, which means it is not in their native formats. Data requirements must be known and set up front to make sure the models and schemas produce usable data for all users.

Key differences between Data Lake and Data Warehouse

How Big Data Platform works

Big Data platform workflow can be divided into the following stages:

1. Data Collection
   Big Data platforms collect data from various sources, such as sensors, weblogs, social media, and other databases.
2. Data Storage
   Once the data is collected, it is stored in a repository, such as Hadoop Distributed File System (HDFS), Amazon S3, or Google Cloud Storage.
3. Data Processing
   Data Processing involves tasks such as filtering, transforming, and aggregating the data. This can be done using distributed processing frameworks, such as Apache Spark, Apache Flink, or Apache Storm.
4. Data Analytics
   After data is processed, it is then analyzed with analytics tools and techniques, such as machine learning algorithms, predictive analytics, and data visualization.
5. Data Governance
   Data Governance (data cataloging, data quality management, and data lineage tracking) ensures the accuracy, completeness, and security of the data.
6. Data Management
   Big data platforms provide management capabilities that enable organizations to make backups, recover, and archive.

These stages are designed to derive meaningful business insights from raw data from multiple sources such as website analytic systems, CRM, ERP, loyalty engines, etc. Processed data stored in a unified environment can be used in preparing static reports and visualizations but also for other analytics and – for example – building Machine Learning models.

Complex Cloud Big Data Platform: AWS, GCP, Azure

Complex Cloud Big Data platforms refer to the cloud-based services offered by the major cloud providers Amazon Web Services (AWS), Google Cloud Platform (GCP), and Microsoft Azure. They are designed for processing and analyzing large, complex data sets.

AWS

AWS provides you with access to a broader ecosystem of tools that comprises many additional tools and features, e.g., AWS Lambda microservices, Amazon OpenSearch Service for search capabilities, Amazon Cognito for user authentication, AWS Glue for data transformation, and Amazon Athena for data analysis, Amazon EMR for processing and analyzing big data, Amazon Kinesis for real-time data processing, and Amazon Redshift for data warehousing, to name a few.

Amazon facilitates the whole process of building a data lake on the cloud and adjusting it to your needs. They automatically configure the core AWS services allowing you to tag, search, share, transform, analyze, and govern specific subsets of data. The AWS solution deploys a console that users can access to search and browse available datasets.

GCP

Google Cloud Platform provides a series of modular cloud services, including computing, data storage, data analytics, and machine learning. According to Google, you can govern purpose-built data and analytic open-source software clusters such as Apache Spark in as little as 90 seconds.

GCP offers a range of services for big data processing, including Google Cloud Storage for data storage, Google BigQuery for fast, interactive data analysis, Google Cloud Dataflow for batch and real-time data processing, and Google Cloud Dataproc for processing big data using Apache Hadoop, Spark, BigQuery, AI Platform Notebooks, and GPUs, and other analytics accelerators.

Azure

Microsoft’s Azure includes all the capabilities required to make it easy for developers, data scientists, and analysts to store. Azure freely integrates with data warehouses, are secure, scalable, and built to the open HDFS standard. As a result, there are no limits to the size of data and the ability to run parallel analytics.

Azure provides a suite of big data services, including Azure Data Lake Storage for storing big data, Azure HDInsight for processing big data using Apache Hadoop and Spark, Azure Stream Analytics for real-time data processing, and Azure Synapse Analytics (formerly SQL DW) for big data warehousing.

Source: azure.microsoft.com

The main differences between AWS, Azure, and GCP

• Services: Azure and AWS both offer a broad range of cloud computing services, while GCP is more focused on big data and machine learning.
• Pricing: AWS is generally considered to be the most expensive, while Azure is the most cost-effective for enterprise customers, and GCP falls somewhere in between.
• Expandability: Azure has strong integration with other Microsoft products, while AWS and GCP have partnerships with various other companies.

Big Data Platform examples

Apache Hadoop

Hadoop is an open-source programming architecture and server software. It is employed to store and analyze large data sets very fast with the assistance of thousands of commodity servers in a clustered computing environment[6]. In case of one server or hardware failure, it can replicate the data leading to no loss of data.

This big data platform provides important tools and software for big data management. Many applications can also run on top of the Hadoop platform. And while it can run on OS X operating systems, Linux, and Windows, it is commonly employed on Ubuntu and other variants of Linux.

Cloudera

Cloudera is a big data platform based on Apache’s Hadoop system. It can handle huge volumes of data. Enterprises regularly store over 50 petabytes in this platform’s Data Warehouse, which handles data such as text, machine logs, and more. Cloudera’s DataFlow also enables real-time data processing.

Source: docs.cloudera.com

Cloudera platform is based on the Apache Hadoop ecosystem and includes components such as HDFS, Spark, Hive, and Impala, among others. Cloudera provides a comprehensive solution for managing and processing big data and offers features such as data warehousing, machine learning, and real-time data processing. The platform can be deployed on-premise, in the cloud, or as a hybrid solution.

Apache Spark

Apache Spark is an open-source data-processing engine designed to deliver the computational speed and scalability required for streaming data, graph data, machine learning, and artificial intelligence applications. Spark processes and keeps the data in memory without writing to or reading from the disk, which is why it is way faster than the alternatives such as Apache Hadoop.

The solution can be deployed on-premise, in addition to being available on cloud platforms such as Amazon Web Services, Google Cloud Platform, and Microsoft Azure. On-premise deployment gives organizations more control over their data and computing resources and can be more suitable for organizations with strict security and compliance requirements. However, deploying Spark on-premise requires significant resources compared to using the cloud.

Read more about Apache Spark machine learning for predictive maintenance

Databricks

Databricks is a cloud-based platform for big data processing and analysis based on Apache Spark. It provides a collaborative work environment for data scientists, engineers, and business analysts offering features such as an interactive workspace, distributed computing, machine learning, and integration with popular big data tools.

Source: databricks.com

Databricks also offers managed Spark clusters and cloud-based infrastructure for running big data workloads, making it easier for organizations to process and analyze large datasets.

Databricks is available on the cloud, but there is also a free community edition that provides an environment for individuals and small teams to learn and prototype with Apache Spark. The Community Edition includes a workspace with limited compute resources, a subset of the features available in the full Databricks platform, and access to a subset of community content and resources.

Snowflake

Snowflake is a cloud-based data warehousing platform that provides data storage, processing, and analysis capabilities. It supports structured and semi-structured data and provides a SQL interface for querying and analyzing data.

It provides a fully managed service, which means that the platform handles all infrastructure and management tasks, including automatic scaling, backup and recovery, and security. It supports integrating various data sources, including other cloud-based data platforms and on-premise databases.

Read more: Leveraging Snowflake for Data Engineering

Datameer

Datameer is a data analytics platform that provides big data processing and analysis capabilities designed to support end-to-end analytics projects, from data ingestion and preparation to analysis, visualization, and collaboration.

Source: datameer.com

Datameer provides a visual interface for designing and executing big data workflows and includes built-in support for various data sources and analytics tools. The platform is optimized for use with Hadoop, and provides integration with Apache Spark and other big data technologies.

The service is available as a cloud-based platform and on-premise. The on-premise version of Datameer provides the same features as the cloud-based platform but is deployed and managed within an organization’s own data center.

Apache Storm

Apache Storm is a free and open-source distributed processing system designed to process high volumes of data streams in real-time, making it suitable for use cases such as real-time analytics, online machine learning, and IoT applications.

Storm processes data streams by breaking them down into small units of work, called “tasks,” and distributing those tasks across a cluster of machines. This allows Storm to process large amounts of data in parallel, providing high performance and scalability.

Read more: Apache Spark machine learning for predictive maintenance

Apache Storm is available on cloud platforms such as Amazon Web Services (AWS), Google Cloud Platform (GCP), and Microsoft Azure, but it is possible to deploy it also on-premise.

Summary: Big data platforms are here to stay

Enterprises are seeking ways to harness big data and draw actionable insights for better decision-making. This is why they are turning to big data platforms since they provide a one-stop solution for all data needs. They help with capturing, curating, storing, searching, sharing, appraisal, and reporting data insights[7]. Based on your needs, you can choose from the big data platforms that we’ve discussed above.

And if you need help along the way, see our big data consulting services. We’ll implement big data solutions for your business to enable you too take full advantage of your data and optimize processes!

Big Data Platform: FAQ

What is big data, and why is it important?

Big data refers to large volumes of data, often characterized by variety, velocity, and complexity, that conventional data management tools struggle to handle effectively. It includes diverse types of data such as mobile phone details, social media content, health records, and transactional data. Big data is important because it enables organizations to derive valuable insights from vast amounts of information, leading to improved decision-making, enhanced customer experiences, and innovation.

What is a big data platform?

A big data platform is an integrated computing solution that combines various software systems, tools, and hardware designed to manage and process large volumes of data efficiently. It provides capabilities for data storage, processing, analysis, and visualization, catering to the diverse needs of businesses dealing with big data. Big data platforms play a crucial role in enabling organizations to harness the power of data for strategic purposes and competitive advantage.

How does a big data platform differ from a data lake and a data warehouse?

While big data platforms, data lakes, and data warehouses all deal with large volumes of data, they serve different purposes and exhibit distinct characteristics:

• Big Data Platform: A comprehensive solution for managing and processing big data, offering capabilities for data storage, processing, analysis, and visualization. It accommodates diverse data formats and supports data at any speed, providing scalability and flexibility.
• Data Lake: A scalable storage repository that holds large volumes of raw data in its native format, enabling organizations to prepare data for various use cases. Data lakes allow for the exploration and analysis of data in its original form, without predefined schemas, offering flexibility and agility.
• Data Warehouse: A structured storage data repository that holds pre-processed data optimized for querying and analysis. Data warehouses require upfront data modeling and schema design, catering to specific business requirements. They offer robust performance and consistency for reporting and analytics.

What are some key features of a good big data platform?

A good big data platform should possess several essential features to effectively manage and process large volumes of data. These features include:

• Scalability: Ability to accommodate growing data volumes and user demands without compromising performance.
• Flexibility: Support for various data formats, processing speeds, and analytical tools to meet diverse business needs.
• Robustness: Reliability and fault tolerance to ensure data availability and integrity, even in the face of hardware or software failures.
• Integration: Seamless integration with existing systems, tools, and technologies to facilitate data interoperability and workflow automation.
• Security: Robust security measures to protect sensitive data assets and comply with regulatory requirements, ensuring confidentiality, integrity, and availability.
• Ease of Use: Intuitive user interfaces and management tools to simplify data operations, administration, and monitoring for users across different roles and skill levels.

How does a big data platform work?

A big data platform typically operates through a series of stages, including data collection, storage, processing, analysis, governance, and management. Data is collected from various sources, such as sensors, weblogs, social media, and databases, and stored in a repository optimized for scalability and performance. It is then processed using distributed computing frameworks and analyzed using analytics tools and techniques. Data governance ensures data quality, security, and compliance, while data management encompasses tasks such as backup, recovery, and archival.

What are some key considerations for choosing a big data platform?

When selecting a big data platform, organizations should consider factors such as data volume, complexity, processing requirements, scalability, cost, and integration with existing systems. It’s essential to assess the platform’s capabilities, performance, security, and support for specific use cases and industry requirements. Additionally, organizations should evaluate vendor reputation, reliability, and long-term viability to ensure a successful implementation and return on investment. Consulting with experienced professionals and conducting thorough evaluations can help organizations make informed decisions and choose the right big data platform for their needs.

The article is an updated version of the publication from Mar 15, 2022.

References

[1] Nytimes.com. The Origins of Big Data. URL: https://bits.blogs.nytimes.com/2013/02/01/the-origins-of-big-data-an-etymological-detective-story/. Accessed March 7, 2022
[2] Waterfordtechnologies.com. Jus Big Data. URL: https://waterfordtechnologies.com/just-big-big-data/. Accessed March 7, 2022
[3] Weforum.org. How Much Data is Generated Each Day. URL: https://www.weforum.org/agenda/2019/04/how-much-data-is-generated-each-day-cf4bddf29f/. Accessed March 7, 2022
[4] Cloudmoyo.com. What is Big Data and Where it comes From. URL: https://www.cloudmoyo.com/blog/data-architecture/what-is-big-data-and-where-it-comes-from/. Accessed March 9, 2022
[5] Khan, I., Naqvi, S.K. Alam, M. Rizvi, S.N.A. (2015). Data model for Big Data in cloud environment. Computing for Sustainable Global Development (INDIACom), 2015 2nd International Conference. pp. 582 -585. Accessed March 9, 2022.
[6] Builtin.com. URL: https://builtin.com/company/hadoop. Accessed March 9, 2022.
[7] NESSI. (2012). Big Data: A New World of Opportunities. Retrieved from: http://www.nessi europe.com/Files/Private/NESSI_WhitePaper_BigData.pdf