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 behaviour, 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 cus­tom­ised, 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 cus­tom­ised 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 optimise 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 visu­al­isa­tion 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 or­gan­ising 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 maximise the eval­u­ation and pro­cessing of very large, con­tinu­ous data streams.

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.