Intro to Big Data Platforms
Big Data platforms are about managing and analyzing a huge amount of data. Take a look at top big data platforms and how they can add value to our lives.
We're swimming in a data sea... and the sea level is rapidly rising.
Data is everything in the modern IT world, as we all know. Additionally, this data keeps growing exponentially every day.
Earlier, we used to talk about kilobytes and megabytes. Today, however, we are referring to terabytes.
Data has no meaning until it is transformed into useful knowledge and information that can assist management in making decisions. It's not clear when "data" became "big data." The latter term most likely originated in Silicon Valley pitch meetings and lunchrooms in the 1990s. What's clearer is how data has exploded in the twenty-first century — according to one estimate, humans will generate 463 exabytes of data per day by 2025 — and how it's accounted for the rise in the use of big data platforms.
Let's explore big data platforms.
What is a Big Data Platform?
Large amounts of data are stored in an organized manner on a big data platform. Aggregated data sets are typically stored on big data platforms, which combine hardware and software tools for data management.
Numerous sophisticated and highly scalable cloud data platforms have emerged to store and process the continuously growing volume of data from various sources because the data influx is persistent and only expected to increase in intensity. Big data platforms are the name given to these kinds of platforms.
A big data platform organizes and stores this volume of data in a way that makes it easy to draw insightful conclusions. Big data platforms use a combination of hardware and software tools for data management to aggregate data on an enormous scale, typically onto the cloud.
Features of Big Data Platform
The following crucial characteristics should be present in any good big data platform:
⦁ Being able to adapt to new applications and tools as needed to meet changing business needs.
⦁ Accept a variety of data formats.
⦁ The capacity to handle large amounts of streaming or static data.
⦁ Possess a large selection of conversion tools to convert data to various preferred formats.
⦁ The ability to support data at any speed.
⦁ Give users the means to search through large data sets for information.
⦁ Support linear scaling.
⦁ The potential for rapid deployment.
⦁ Having the tools needed for data analysis and reporting needs.
How do Big Data Platforms Work?
The stages of a big data platform workflow are as follows:
Big data platforms gather information from various sources, including sensors, weblogs, social media, and other databases.
Once the data has been gathered, it is stored in a repository like Google Cloud Storage, Amazon S3, or Hadoop Distributed File System (HDFS).
Data processing tasks include aggregating, filtering, and transforming the data. Distributed processing frameworks like Apache Spark, Apache Flink, or Apache Storm can be used.
Data is processed and examined using analytics tools and methods like data visualization, predictive analytics, and machine learning algorithms.
The completeness, accuracy, and security of the data are ensured by data governance, which includes data quality management, data cataloging, and data lineage tracking.
Big data platforms offer management tools that let businesses create backups and recover and archive data.
These steps are intended to produce actionable business insights from unstructured data from various sources, including CRM, ERP, loyalty engines, website analytics systems, etc. It is possible to create static reports and visualizations using processed data that has been stored in a unified environment, as well as perform other analytics, like creating machine learning models.
Need for Big Data Platform
Every feature and capability of numerous big data applications are combined into one solution by a big data platform. The main components are servers, storage, databases, management tools, and business intelligence.
It also emphasizes giving users effective analytics tools for massive datasets. Data engineers frequently use these platforms to gather, clean, and prepare data for business analysis. Using a machine learning algorithm, data scientists can use this platform to find relationships and patterns in massive data sets. These platforms allow users to create applications specifically for their use cases, such as calculating customer loyalty (an E-Commerce use case), among countless other use cases.
Examples of Big Data Platforms
Let's look at some of the big data platforms.
GCP-Google Cloud Platform
Google Cloud Platform offers modular cloud services such as computing, data storage, analytics, and machine learning. Google claims you can manage purpose-built data and analytics open-source software clusters like Apache Spark in as little as 90 seconds.
GCP provides a variety of big data processing services, including
⦁ Google Cloud Dataflow for real-time data processing
⦁ Google BigQuery for fast, interactive data analysis
⦁ Google Cloud Storage for data storage,
⦁ Google Cloud Dataproc for big data processing using Apache Hadoop, Spark, BigQuery, AI Platform Notebooks, GPUs, and other analytics accelerators.
Amazon Web Services provides you with access to a broader ecosystem of tools that includes many additional tools and features, such as
⦁ AWS Lambda microservices
⦁ Amazon OpenSearch Service for search capabilities
⦁ Amazon Cognito for user authentication
⦁ AWS Glue for data transformation
⦁ Amazon Athena for data analysis
⦁ Amazon Kinesis for real-time data processing
⦁ Amazon Redshift for data warehousing
Amazon simplifies the entire process of creating and customizing a data lake in the cloud. They automatically set up the fundamental AWS services, allowing you to tag, search, share, transform, examine, and control particular data subsets. The AWS solution includes a console from which users can search and browse available datasets.
Microsoft's Azure includes all the capabilities to make it simple for developers, data scientists, and analysts to store data. Azure integrates easily with data warehouses, is secure and scalable, and adheres to the open HDFS (Hadoop Distributed File System) standard. As a result, there are no restrictions on data size or the ability to run parallel analytics.
Azure offers a variety of big data services, including,
⦁ Azure Data Lake Storage for big data storage
⦁ Azure HDInsight for big data processing with Apache Hadoop and Spark
⦁ Azure Stream Analytics for real-time data processing
⦁ Azure Synapse Analytics for big data warehousing.
Cloudera is a big data platform built on Apache Hadoop. It can handle massive amounts of data. Enterprises frequently use this platform's data warehouse to store over 50 petabytes of data, including text, machine logs, and other types. DataFlow from Cloudera also supports real-time data processing.
Cloudera's platform is based on the Apache Hadoop ecosystem and includes, among other things, HDFS, Spark, Hive, and Impala. Cloudera offers a complete solution for managing and processing big data, including data warehousing, machine learning, and real-time data processing. The platform is available for on-premises, cloud, or hybrid deployment.
Hadoop is a programming architecture and server software that is open source. It quickly stores and analyses large data sets using thousands of commodity servers in a clustered computing environment. If a server or hardware fails, it can replicate the data, resulting in no data loss.
This big data platform includes essential tools and software for big data management. Many applications can run on top of the Hadoop platform as well. While it can run on OS X, Linux, and Windows, it is most commonly used on Ubuntu and other Linux variants.
A free and open-source data processing engine called Apache Spark gives applications for streaming data, graph data, machine learning, and artificial intelligence computational speed and scalability.
Spark processes and stores data in memory rather than writing to or reading from a disc, which makes it much faster than alternatives such as Apache Hadoop.
The solution is available on-premises and on cloud platforms such as Google Cloud Platform, Microsoft Azure, and Amazon Web Services. On-premises deployment gives organizations greater control over their data and computing resources and may be more appropriate for organizations with stringent security and compliance requirements. However, deploying Spark on-premises requires significant resources compared to using the cloud.
Snowflake is a cloud-based data warehouse platform for storage, processing, and analysis. It can handle structured and semi-structured data and has a SQL interface for querying and analyzing data.
It offers a fully managed service, meaning the platform takes care of all infrastructure and management tasks, such as automatic scaling, backup and recovery, and security. It enables the integration of many different data sources, including on-premise databases and other cloud-based data platforms.
Databricks is an Apache Spark-based cloud platform for big data processing and analysis. It offers a collaborative work environment for data scientists, engineers, and business analysts, an interactive workspace, machine learning, distributed computing, and integration with well-known big data tools.
Databricks also provides managed Spark clusters and cloud-based infrastructure for running big data workloads, allowing businesses to process and analyze large datasets more efficiently.
Databricks is available in the cloud, but a free community edition allows individuals and small teams to experiment with Apache Spark. The Community Edition offers access to a portion of the community's content and resources, a workspace with constrained compute resources and a subset of the features in the full Databricks platform.
Datameer is a big data processing and analysis platform that supports end-to-end analytics projects, from data ingestion and preparation to visualization, analysis and collaboration.
Datameer features a visual interface for designing and executing big data workflows, built-in support for a variety of data sources, and analytics tools. The platform is designed to work with Hadoop and integrates with Apache Spark and other big data technologies.
The service is available both on-premises and as a cloud-based platform. Datameer's on-premises version, deployed and managed within a company's data center, offers the same features as the cloud-based platform.
Real-time analytics, online machine learning, and Internet of Things (IoT) applications can all benefit from Apache Storm, a distributed processing system that is free and open-source and capable of processing large amounts of data in real time.
Storm processes data streams by dividing them into manageable work units, or "tasks," and then distributing those tasks among a cluster of computers. As a result, Storm has high performance and scalability and can process massive amounts of data concurrently.
Apache Storm can be deployed on-premises as well as on cloud platforms like Google Cloud Platform (GCP), Amazon Web Services (AWS), and Microsoft Azure.
Benefits of Big Data Platform
How do streaming services like Netflix and Spotify know what you want to watch next? This is largely a result of big data platforms operating in the background.
In almost every industry, from healthcare to retail and beyond, having a solid understanding of big data has become advantageous. These platforms are used by businesses more and more to gather massive amounts of data and transform it into organized, actionable business decisions. Thus, companies can better understand their target markets and customers, find new markets, and predict their next moves.
Utilizing enterprise data platforms is crucial for maintaining a competitive edge and staying on top of consumer trends, rival brands, and competing products.
Future of Big Data
The vast majority of big data experts agree that the amount of data generated will continue to grow exponentially in the future. According to some estimates, the global data sphere will reach 175 zettabytes by 2025. The growing number of internet users who do everything online and the proliferation of connected devices and embedded systems are significant contributors.
Experts predict that the future of big data will be cloud-based as public and enterprise cloud services providers such as Google Cloud Platform, Microsoft Azure, and Amazon Web Services (AWS) transform how big data is stored and processed. Hybrid and multi-cloud environments are the future of corporate big data project deployment.
So-called Fast data, which allows for processing in real-time streams, is expected to grow in popularity. Fast data will become a critical vehicle for delivering quick business value, with stream processing technologies allowing organizations to analyze such information in as little as one millisecond. This trend is expected to be fueled by the incorporation of evolving machine learning and artificial intelligence technologies into big data analytics tools.
Be prepared for big data analytics in the future.
Despite the fact that many large corporations have already embraced some cost barriers, these trends, and the future of big data analytics, are no longer constrained. This provides them with an advantage over their competitors.
Small and mid-size businesses will use big data analytics significantly more in the future.
The future is promising for those who take steps to comprehend and embrace it.
And if you need help along the way, see our big data consulting services. We’re always happy to help!