What Is BDaaS About?
After SaaS, IaaS and PaaS (for Service, Infrastructure and Platform as a Service respectively), a new acronym is on the rise in the IT sector. BDaaS, stands for Big Data As A Service. This form of cloud computing works in a similar way to SaaS and IaaS. The key difference being that, in this case, cloud providers sell access to data platforms and tools instead. The aim is to enable organisations to process, manage and analyse large volumes of data without burdening their IT systems and organisational resources. This allows companies to use third-party IT expertise rather than deploying in-house systems. BDaaS can even become a competitive advantage if properly understood and exploited. In this article, you will find out how BDaaS works in practice and what its advantages are.
How does Big Data as a service work?
The main components of BDaaS offers
Big Data as a Service takes the form of dedicated software and systems in the cloud or in a hosted service, managed and operated by a cloud provider.
The BDaaS market is currently dominated by 3 main providers:
● Amazon Web Services’ (AWS) Amazon EMR;
● Google Cloud's Dataproc offering;
● and Microsoft’s Azure HDInsight.
Although these 3 providers’ offerings are somewhat different, they generally include:
● the Hadoop framework for the design of distributed applications;
● the Tez framework for creating interactive data processing programs;
● HBase, the complementary database to Hadoop;
● Oozie scheduling and workflow software;
● the Apache Spark processing engine;
● Apache Hive or Presto, the data warehouse infrastructure and SQL query engine;
● analysis tools such as Jupyter Notebook, Zeppelin or Pig;
● programming languages such as Python, R and Scala.
BDaaS data is most often stored in the Hadoop Distributed File System (HDFS) or in cloud object storage services such as Amazon Simple Storage Service, Azure Blob and Google Cloud Storage. Finally, BDaaS platforms can also connect to cloud data warehouse environments such as Azure Data Lake Storage, Iceberg, Delta Lake, and Snowflake.
Use cases for Big Data as a service
BDaaS offers different types of data analysis to businesses. These can range from scientific studies to business and marketing. For example, an organisation can use it to measure the impact of an SEO campaign.
IT professionals also use BDaaS for:
● POC (Proof Of Concept): BDaaS enables the feasibility of a project to be validated by testing it with an appropriately-sized infrastructure and without costly hardware investments;
● predictive analysis, which allows Big Data to be used and interpreted in order to understand markets and adapt strategic directions;
● peak load management: organisations can use temporary clusters for scalability and then remove them afterwards without the need to change their internal IT;
● Disaster Recovery Planning (DRP): organisations can synchronise large volumes of data to BDaaS platforms so that they can access and restart their business quickly in the event of an internal incident.
The list is of course not exhaustive, as the possibilities offered by Big Data as a Service are numerous. Various companies, even outside the IT sector, regularly use it in their activities. This solution has major advantages compared to investing in in-house infrastructure and storage.
The advantages of BDaaS
The financial argument is generally the first to be put forward for BDaaS and other “accessible as a service” solutions.
This is because data storage and analysis is not limited to the (often already expensive) purchase of servers. Maintenance (of software and hardware), physical security (creation of secure, partitioned rooms, etc.) and application security, including prevention of cyberattacks and internal vulnerabilities, must also be taken into account. Finally, the cost of software licences must also be taken into account.
With BDaaS, it is the cloud service provider who takes charge of all the infrastructure and maintenance for these servers. The access fees for hosted solutions represent
a fraction of the cost of locally installed software. In addition, companies usually only pay for the time they use or the amount of storage space they use.
Providers of BDaaS and more generally of cloud offerings are subject to scrutiny and inspection with regard to data security. They also have to comply with strict norms and standards. Of course, this does not mean delegating all responsibility for cybersecurity to a cloud provider.
BDaaS must be integrated into the company’s internal security policy, including:
● choosing a secure cloud provider;
● access and authorisation controls for BDaaS platforms;
● network monitoring to monitor access and permissions.
On the other hand, the use of BDaaS reduces expenses and often means less human resources spent for security maintenance. The cloud provider performs the security updates to its servers and software.
Better data availability with BDaaS
Let us take the example of an organisation that stores its data on an internal server. What happens in the event of hardware failure or attacks? Unless it has invested in an off-site backup or a redundant array of independent disks (RAID), all data and applications based on the server are unavailable for an indefinite period of time.
Cloud and BDaaS providers include redundancy as a standard feature in their offerings. This means that data is not stored on a single server, but replicated on several machines which are, except in rare cases, in several locations in a data centre or even in data centres spread around the world. This replication also allows the data to be stored as physically close as possible to the users, to limit latency times. Most providers guarantee 99.99% availability.
BDaaS therefore meets many business needs, whatever their field of activity. Its advantages make it a solution that is increasingly used in the professional world. Jobs and missions as Big Data architects, Big Data experts or Big Data cloud DevOps are multiplying.
As an IT professional, have you ever worked with BDaaS environments? Share your stories with us in the forum.
Azure HDInsight: https://azure.microsoft.com/en-us/services/hdinsight/