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.

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.