Big Data as a Service: this is how BDaaS works

Big Data analysis offers great competitive advantages for the scalability and security of companies. Therefore, cloud platforms based on the principle of Big Data as a Service play an important role in real-time analysis, storage and processing of large amounts of data. 

What does Big Data as a Service (BDaaS) mean?

BDaas is able to process large amounts of data coming from business processes, customer trends, sales, and security analytics in real time. However, not all companies can afford to have local cloud computing servers . Local servers that are responsible for storing, analyzing and evaluating Big Data also involve a waste of time and a high cost. It is precisely in these cases where BDaaS is most useful.

The term BDaaS combines the most important custom software development services and tools for storing and processing huge amounts of data. Among these services are the following:

  • SaaS (Software as a Service)
  • IaaS (Infrastructure as a Service)
  • PaaS (Platform as a Service)
  • HDaas (Hadoop as a Service)
  • Data Analytics as a Service

Through this general approach, BDaaS also comes closer to the XaaS principle , which stands for “Anything as a Service.” Evaluating data volumes, both structured and unstructured, requires storage, networking, and computing capabilities . This is exactly what BDaaS offers through a cloud platform that includes analytics services and almost unlimited storage volume. By outsourcing Big Data tasks, companies not only save time and money, but also increase their scalability, security, and flexibility .

What features does Big Data as a Service include?

Specialists in BDaaS offerings include large IT companies such as Amazon, Microsoft and Google. The services and features offered by BDaaS plans include analytics and statistics services, data mining software , cloud platforms, and data management tools. Depending on your needs and the project you have in mind, BDaaS functions can be adapted and tools added or removed according to the principle of on-demand computing .

Key features of BDaaS include:

Multifunctional Service Oriented Architecture (SOA)

BDaaS uses the distributed computing and processing capabilities of a connected digital infrastructure. Since this on-premise modality involves high maintenance and costs, the advantages of distributed computing are taken advantage of and company costs are reduced at the same time. With a service-oriented architecture, custom service packages can also be chosen for data analysis and processing as needed.

Horizontal scaling

BDaaS uses a network of powerful hardware and software components, as well as select tools, to maintain flexibility through scale out. This way, you don’t need your own fixed infrastructure, you simply choose the cloud features you want for processing your data. BDaaS services allow tasks and processes to be shared, mainly through storage architectures such as Apache Hadoop , which rely on clusters of computers and computing nodes to carry out large-scale processes continuously and quickly.

From Big Data to Smart Data

BDaaS takes large amounts of messy data and creates structured Smart Data thanks to its data-driven Marketing approach . Modern software applications and data warehouses evaluate large amounts of data for you and create statistics and reports based on that data. These reports and statistics allow you to optimize your Business Intelligence and the strategic direction of your company.

Security and expansion of companies

Data processing and analysis using BDaaS sheds light on various potentials, growth opportunities, security gaps and inefficiencies in business processes  such as software development services and their infrastructure. Through data models, statistics and predictive analysis , it is not only possible to plan the scalability of the company in the long term, but also to strategically align it through data-driven analysis. 

A look at the main components of BDaaS

The tools included in a BDaaS plan depend on the provider. They typically include several Big Data software, i.e. data warehouse systems and Big Data Frameworks (e.g. Apache Hadoop) with their main components: Hadoop Distributed File System (HDFS) and MapReduce. Hadoop is used to store, aggregate, analyze and process Big Data in a distributed way in the cloud . Other major BDaaS components and systems for distributed computing and processing include, but are not limited to:

  • Apache Spark : open source and in-Memory System framework for parallel processing of Big Data through clustering with Hadoop and machine learning
  • Apache Hive Data warehouse system for Big Data queries and analysis based on Apache Hadoop
  • Java, Python, R and Scala: most used programming languages ​​for Big Data projects
  • Analysis tools such as Jupyter Notebook, Zeppelin and Mahout: important analysis and visualization tools for large volumes of data that can be used with Hadoop through Big SQL
  • Apache Flink: a Steam Processing Framework for non-stop processing of big data streams in real time
  • Oozie Workflow, Sqoop, ZooKeeper: important management tools to manage workflows, data transfers from SQL databases and to organize Hadoop services
  • Presto: A SQL Query Engine for Fast, Interactive Big Data Retrieval and Analysis

In what cases is BDaaS used?

Where BDaaS is used is closely related to how Big Data as a Service is used. We present the most important types of BDaaS and how to apply them:

Core BDaaS

This is a basic version of BDaaS with basic services, which include a cloud-based Hadoop Framework and several open source tools for data analysis, query and processing, such as Hive.

BDaaS Performance

The Performance version offers to completely outsource Big Data analytics to Hadoop infrastructures with powerful analysis and management tools. It is a perfect solution for strategic growth plans and on-demand scalability.

Feature BDaaS

It is recommended for companies with specific requirements for analyzing and processing large data streams. Analysis services and data queries can be used independently of the specific cloud provider through web and programming interfaces and database adapters. All this thanks to specific tools that go beyond the standardized Hadoop Framework.

Integrated BDaaS

Integrated BDaaS is a kind of complete package, it combines the performance-oriented approach of Performance BDaaS and the flexibility of Feature BDaaS. This tool allows companies to maximize the analysis and processing of large, continuous data streams.

A look at the benefits of BDaaS

Companies that choose BDaaS benefit from the following advantages:

  • They reduce personnel, infrastructure and maintenance costs by outsourcing Big Data processes
  • They allow small or medium-sized companies without an appropriate custom software development company infrastructure to also analyze large amounts of data.
  • Maximum performance and scalability thanks to distributed computing and Clustering
  • High data security and protection against data loss and cyber attacks through a modern and protected cloud infrastructure
  • On-demand computing with optional tools and services based on project needs and size
  • They optimize the strategic direction of business processes through Big Data analysis and forecasts
  • Compliance with data protection and compliance regulations
  • Almost unlimited storage capacity for Big Data
  • Processing and analyzing huge amounts of data in real time , regardless of the cloud provider

In summary: Who is Big Data as a Service designed for?

Big Data and data-based decisions represent a fundamental aspect in the success and growth of companies. Due to the increasing digitalization and growth of the e-commerce market, the evaluation and storage of Big Data offers a significant competitive advantage . This is especially interesting for companies that need scalable and structured data analysis, but lack the resources and capacity to maintain IT infrastructure and knowledge. In this way, large companies in the banking, security, communications, media, education, wholesale and retail sectors use practically unlimited capacities; even for large Big Data processes.

Both small and medium-sized businesses and large companies and institutions can rely on BDaaS not only for its elastic “on-demand” scalability, but also for its real-time analytics of large data streams and its almost unlimited storage capacities. This reinforces the long-term strategic direction of business processes and creates, with a relatively low investment, a powerful Big Data infrastructure.

 

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