How does Big Data as a Service (BDaaS) work?

Big data analysis offers companies a considerable competitive advantage when it comes to scalability and security. Therefore, cloud platforms based on the Big Data as a Service principle play an important role in the real-time analysis, storage and processing of big data. Before we begin, it is important to understand what services are included with BDaaS and what advantages they offer.

High-performance IT infrastructures are essential for companies who want to benefit from competitive advantages and remain capable of growth. Companies must be able to process large amounts of data from business processes, customer behavior, sales and security analyses in real time. However, not every company can afford cloud computing with on-premises systems. On-premises departments which deal dig data storage, analysis, and reporting also require time and demand high costs. This is where BDaaS comes in.

BDaaS is an umbrella term, and it combines the most important services and tools for storing and processing huge amounts of data. These include:

• 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

BDaaS’ integrated approach is similar to the XaaS principle, which means “Anything as a Service”. Evaluating structured and unstructured data volumes requires storage, network and computer capacities. This is exactly what BDaaS offers on a cloud platform. It includes analysis services and almost unlimited storage volume. Outsourcing big data tasks not only allows companies to save time and money, it also increase their scalability, security and flexibility.

BDaaS specialists include major IT companies such as Amazon, Microsoft and Google. BDaaS packages include services and functions for analysis and statistics services, data mining tools, cloud platforms and data management tools. Depending on the requirements and project, BDaaS functions can be customized, and tools can be added or removed according to the on-demand computing principle.

BDaaS core features include:

BDaaS uses the distributed computing and processing capabilities of connected digital infrastructure. This on-premises results in high costs and maintenance, so you leverage the strengths of distributed computing while reducing your business costs. A service-oriented architecture also allows you to choose customized service packages for data analysis and processing.

You remain flexible through horizontal scaling (scale out) by using selected tools and the powerful components hardware and software components in a network. You only choose cloud-based capacities which you need for data processing, and you do not require your own static infrastructure. You share tasks and processes with BDaaS services, mostly through storage architectures such as Apache Hadoop. These build on computer clusters and computer nodes to process large processes continuously and quickly.

BDaas focuses on data-driven marketing and creates structured smart data from complex data volumes. Modern software applications and data warehouse systems can evaluate mountains of data and create data-based statistics and reports. You can optimize your business intelligence and your company’s strategic orientation using these tools.

BDaaS’ data processing and analysis highlights the various potentials, growth opportunities, security gaps and inefficiencies in business processes and infrastructure. Data models, statistics and predictive analytics make it possible not only to plan the scalability of the company in the long term, but also to strategically align the company through data-based analyses. In addition, BDaaS providers ensure that all data processes comply with current regulations on data protection and compliance.

The tools included in a BDaaS package depend on the provider. In most cases, it involves several bundles of big data software such as data warehouse systems and Big Data frameworks such as Apache Hadoop with the core components Hadoop Distributed File System (HDFS) and MapReduce. Hadoop is used for distributed, cloud-based storage, aggregation, analysis, and big data processing. Other BDaaS core components and systems for distributed processing and computing include:

• Apache Spark: An open-source framework and in-memory system for parallel big data processing which use clustering with Hadoop and self-learning systems
• Apache Hive: A data warehouse system for big data queries and Apache Hadoop analysis
• Java, Python, R and Scala: The common programming languages for big data projects
• Analytics tools like Jupyter Notebook, Zeppelin, and Mahout: The key analytics and visualization tools for big data which can be used with Hadoop via Big SQL
• Apache Flink: A stream processing framework for uninterrupted real-time big data stream processing
• Oozie Workflow, Sqoop, ZooKeeper: The key management tools for managing workflows, data transfers from SQL databases, and organizing Hadoop services
• Presto: An SQL query engine for fast, interactive big data retrieval and analysis

How BDaaS is used is depends on how Big Data as a Service is used. We’ll present the most important application forms and BDaaS types:

This is a basic version of BDaaS with basic services such as a cloud-based Hadoop framework and various open-source tools for analytics, querying and data processing such as Hive.

The Performance version provides comprehensive big data analytics offloading to Hadoop infrastructures with powerful analytics and management tools. It is suitable for strategic growth plans and on-demand scalability.

This is recommended for companies with specific requirements for large data stream analysis and processing. Specific tools which go beyond the standard Hadoop framework, analytics services and data queries can be used independently of specific cloud providers through web and programming interfaces and database adapters.

Integrated BDaaS

Integrated BDaaS is a like an all-round package which combines the performance-oriented approach of Performance BDaaS and the flexibility of Feature BDaaS. This package enables companies to maximize the evaluation and processing of very large, continuous data streams.

Integrated BDaaS is a like an all-round package which combines the performance-oriented approach of Performance BDaaS and the flexibility of Feature BDaaS. This package enables companies to maximize the evaluation and processing of very large, continuous data streams.

Advantages of BDaaS at a glance

Companies that opt for BDaaS benefit from the following advantages:

• Reduces costs for personnel, infrastructure and maintenance by outsourcing Big Data processes
• Enables even small or medium-sized companies to analyze large amounts of data without a suitable IT infrastructure
• Maximum performance and scalability through distributed computing and clustering
• High data security and protection against data loss and cyber-attacks using modern, protected cloud infrastructure
• On-demand computing with optional tools and services based on requirement and project size
• Optimizes the business processes’ strategic alignment through big data analytics and forecasting
• Adherence to data protection and compliance regulations
• Almost unlimited storage capacities for Big Data
• Processing and evaluation of enormous amounts of data in real time independent of the cloud provider

Big data and data-driven decisions can have a significant influence on a company’s success and growth. Due to increasing digitalization and the growing e-commerce market, the evaluation and storage of big data offers a significant competitive advantage. This is especially important for companies who need scalable, structured data analytics but lack the resources and capacity for the infrastructures and IT expertise. Large companies in the banking, security, communications, media, education, and wholesale and retail sectors are using unlimited capacities for large-scale big data processing.

Small and medium-sized enterprises or large companies and institutions can all rely on BDaaS not only for elastic scalability on demand, but also for real-time analyses of large data streams and almost unlimited storage capacities. This strengthens the long-term strategic alignment of business processes and creates a powerful big data infrastructure for relatively low investments.