Every day, almost 2.5 quintillion bytes of data are generated. 50% of data will be stored in the cloud by 2025, rising from 25% in 2015. And the global data analytics market is expected to reach $302.01 billion by 2030 with a CAGR of 28.7% from 2025 to 2030.
Too many figures, right? But why so much buzz around data? The answer is simple: the world today is operating on data, from selling you a pen to selling you shares & stocks, businesses and markets are harnessing data. They use Big Data Analytics as a Service for transforming massive amounts of raw, unstructured data into actionable insights. In this article, I will take you through this combination of big data analytics, machine learning & business intelligence.
What is Big Data Analytics as a Service (BDAaaS)
BDAaaS is basically a cloud-based model that is capable of vast amounts of data storage and processing. It is a service used by organizations to analyze their large data sets without worrying about on-premise infrastructure, hiring data experts, or software. It is a form of cloud computing similar to SaaS, PaaS, and Infrastructure as a Service.
How Does it Work?
Companies generate large amounts of unstructured, semi-structured, and structured data from multiple sources and departments regularly.
The data is collected by the cloud solution provider, and following this, it is stored in storage services. Then it is filtered, separated into formats, and cleaned for the analysis process. Now the analysis process is done to identify patterns, trends, or spot forward-looking information. Later, these analyzed results are turned into visuals and reports for taking necessary business decisions, such as planning ongoing operations, future strategies, or making any instant changes.
Importance of Big Data Analytics as a Service
Companies are heavily investing in cloud-based services because they save money, time, and other resources. Just as many other cloud services, BDAaaS is basically about outsourcing your big and time-consuming tasks. Here, companies outsource their data management and analysis to expert providers.
- Data serves you with billions of revenues, but only if handled and decoded properly.
- Handling data requires a team of expert data scientists or engineers.
- It requires specific and expensive software, servers, and hardware.
Providing all this under one umbrella is the biggest benefit of cloud-based big data analytics; you don’t need to buy, hire, or build anything or anyone. Just rent services as per your need and pay the providers as you use them.
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Data Lake Vs. Data Warehouse
A data lake is a big pool where all structured and unstructured data is kept, and the purpose of this data is undefined; it’s basically like a dump yard of data. Whereas a data warehouse is like an organized historical library where all data is stored after being cleaned, categorized, and processed for analysis.
| DATA LAKE | DATA WAREHOUSE |
|---|---|
| Store all types of data: raw & unstructured, and the data can be stored indefinitely for instant or future use | Stores only processed and structured data for analysis, and is used for targeted business needs |
| Used by data experts (scientists & engineers) | Used by business managers, decision makers, and analysts |
| Uses predictive analysis, ML, data visualization, BI, and big data analytics | Uses data visualization, BI, and data analytics |
| Schema is defined after data storage (schema-on-read) for faster data capture and storage | Schema is defined before data storage (schema-on-write), a long process, but once done, it can be of instant and future use |
| Involves ELT (Extract, Load, Transform), the data is extracted from the source and structured only when needed | Involves ETL (Extract, Transform, Load), the data is extracted, structured, and ready for business analysis |
| Inexpensive compared to a data warehouse, and lower operational costs due to a simple process | More expensive than data lakes, requires managing, leading to additional operational costs |
Big Data Integration Tools
- Apache Kafka: It is an open-source streaming system with high throughput and durability, best for event-driven data pipelines and real-time data feeds.
- AWS Glue: Amazon Kinesis, AWS Data Pipeline, AWS Lake Formation, and Amazon EMR are all tools in Amazon’s big data integration solution ecosystem, each serving a unique purpose.
- Microsoft Azure Data Factory: Used for seamless integration between on-premise and cloud sources.
- IBM DataStage: Widely used big data integration tool for ETL & ELT processes. The modern version is supported on IBM Cloud Pak for Data, for multicloud and hybrid cloud setups.
- Apache NiFi: A data flow automation tool offering a visual interface. It is useful for routing and transforming data from multiple sources.
Conclusion
Simply put, cloud computing is no longer an optional thing and is now a crucial and widespread aspect across businesses. Companies have shifted their way of handling and managing data from building data stacks to leveraging cloud-based, scalable data analytics services. BDAaaS providers like AWS, Google Cloud, and Microsoft Azure are giving rise to easy, profitable, faster, flexible, and consistently growing ways of turning data into revenue-generating steps. Growing at a CAGR of 20.4% the global cloud computing market is projected to reach up to $2,390.18 billion by 2030.
If you are stacking floppies to manage data, you are lagging. Use the power of business intelligence and data insights, and many other transformative technologies involved in Big Data Analytics as a Service, and transform your business.
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