The term “data” has already changed for good. Now -no surprises here- it’s all about “big data.” Its explosion into the 21st century led to an ever-increasing amount of information which, by 2025, will represent a flow of 463 exabytes of data per day.
This huge stream of data doesn’t seem to decelerate any time soon. Every week, groundbreaking technologies emerge and create new potential sources of information. How to store, manage and make sense of all this data? Here is when big data platforms appear to save the day.
What a Big Data Platform is and Why it is Key for Businesses’ Success
A big data platform is an integrated computing solution that combines data management hardware and software tools. These highly sophisticated platforms store and process data on a massive scale, usually on the cloud. Their purpose is to structure data and make it understandable for businesses to extract actionable insights.
The power of big data platforms comes from one fact: they put together all big data tools and applications into one single solution. They integrate servers, storage, databases, management utilities, and business intelligence. Due to their diverse set core, their efficiency is wild. Some of them are able to manage streaming data or data conversion between formats in the blink of an eye.
Companies increasingly rely on these platforms, no matter the industry. From retail to healthcare, big data platforms became the key to staying ahead of the curve with customers, competitors, and market trends.
Big Data Platforms’ Essential Features And Workflow
Any respected big data platform should have the following components:
- Scalability for data analysis and storage capacity.
- Data ingestion, ETL (Extract, Transform, and Load), and warehouse resources.
- Real-time stream computing.
- Machine learning and advanced analytics.
- Data governance for data protection.
- Reduced latency.
With these features at full speed, big data platforms are unbeatable at providing meaningful business insights. But, how do they work? The path begins with tons of raw data from multiple systems such as CRM (Customer Relationship Management), ERP (Enterprise Resource Planning), loyalty engines, and website analytics systems. The journey’s destiny relies on sophisticated reports. In between, data goes through 6 stages:
Stage 1: Data Collection
Big data platforms gather data from various sources of information and databases. They range from sensors and IoT devices to weblogs and social media.
Stage 2: Data Storage
After being received by the platform, the data is saved in a repository. Some examples of repositories are HDFS (Hadoop Distributed File System), Google Cloud Storage, or Amazon S3.
Stage 3: Data Processing
Now it’s time to take action. Data can be transformed into new formats, filtered, and combined to be structured in a certain way. Apache Spark, Apache Flink, or Apache Storm are potentially distributed processing frameworks in charge of this stage.
Stage 4: Data Analysis
Once the data is processed, it is analyzed by big data analytic technologies like ML algorithms, predictive analytics, and big data visualization.
Stage 5: Data Quality Assurance
At this stage, it’s key to ensure the accuracy, completeness, reliability, relevance, and timeliness of the data. Some of the processes involved are data cataloging, data lineage tracking, and data quality management.
Stage 6: Data Management
Finally, big data platforms provide the possibility of backup, recovering, or archiving data.
Best Big Data Platforms
Amazon Web Services
Amazon Web Services is Amazon’s cloud-based big data platform. It comes fully packed with a broad ecosystem of analytics tools. With all its resources placed in a secure cloud-based environment, AWS provides cool features that scale along with data growth such as customizable encryption or access to a virtual private cloud.
Its analytics tools are designed for everything from data preparation and warehousing to data lake building. Thanks to its automatically configured core services, with AWS you can tag, browse, share, transform, analyze, and govern datasets.
AWS gives users access to a wide ecosystem of additional tools, including:
- AWS Lambda microservices for serverless data management
- OpenSearch Service for data search
- Cognito for user authentication
- AWS Glue for data transformation
- Athena and EMR for data analysis
- Kinesis for real-time data processing
- Redshift for data warehousing
Google Cloud
Google’s big data platform is one of the most renowned in the market. With Google Cloud Platform, you can manage purpose-built data with a set of easy-to-combine modular services. Each tool has its own specialty and the list is endless. Here are just a few of its main features:
- Cloud Storage for object storage
- BigQuery for data analysis and insights democratization
- Dataflow for batch and stream data processes unification
- Compute Engine for virtual machines creation
- Vision AI for insights imaging with ML models
Microsoft Azure
Microsoft’s big data platform, Azure, combines the force of open-source Apache technologies with native analytics tools. It allows developers, scientists, and analysts to streamline data cluster analysis, without data size limitations.
Its suite of big data services includes:
- Data Lake Storage for big data storage
- HDInsight for big data processing with Apache Hadoop and Spark
- Stream Analytics for real-time data processing
- Synapse Analytics for big data warehousing
Companies of all sizes and from all industries seek better actionable insights. Data-driven decisions are essential in this fast-paced global market. In this context, big data platforms are the quintessence of business success. If you need guidance to navigate the big data world, we are here to help! Give us a shout at vanguard-x.com and check out our Big Data Analytics Services.
