In 2008, Christophe Bisciglia from Google, Amr Awadallah from Yahoo!, Mike Olson from Oracle and Jeff Hammerbacher from Facebook came together as founders of Cloudera, a company with the core belief that open source is the future. Today, with its headquarters at Pal Alto, California, it operates in over 24 countries in the world. In this blog we will be talking about SDX, Shared data experience – which is Cloudera’s secret recipe to deploy data engineering, data science, analytical DB and optional DB all on a single platform.
What is SDX?
As mentioned above, Shared Data Experience is a fundamental capability of Cloudera Enterprise that makes multidisciplinary analytics easier to develop, cheaper to deploy, and secure. Its core functions are:
- Data Engineering: It helps run batch or stream processes that ease out training machine learning models and speed ETL processes.
- Data Science: It enables businesses to explore big data with hundred percent securities.
- Analytic DB: It gives the fastest time-to-insight and works with all sorts of data types and in all environments possible.
- Operational DB: It helps deploy applications that are data-driven and serve real-time insights.
Each of these functions is extremely crucial, even as standalone functions, for all business enterprises indulging in data analysis. However, most of them require a combination of two or more of these functions applied together. This is because industry giants have big data to manage, as well as analyze.
Benefits of Cloudera SDX
First, the procurement cost is reduced because it’s always cheaper to buy multiple functions on one platform rather than buying multiple platforms. Second, SDX makes it possible to reduce infrastructure costs by removing redundancies and inefficiencies. Third, SDX reduces operation costs by enabling a single operations team to handle all big data operations rather than having one team for every platform or management software.
- Increases Speed
Once you’re hooked onto SDX you do not need lengthy service contracts to make things work. This is because now the users are working with a software that allows sharing data out-of-the-box, hence reducing deployment time. It also cuts down the time to launch applications by presetting context like governance, security, etc. Since SDX inherits all the best practices from other tenants, it reduces time to onboard new tenants.
- Increases Ease
While using SDX, you only need to define one security protocol that will be applicable on all applications and user platforms. This makes security a pretty easy task as compared to managing multiple protocols. Also, governance is made easy on SDX as you have to maintain a single record of all technical and business terms that can be shared as one data log to all the users.
It also allows various people associated to discover new data sets and lineages on their own, setting up a self-service environment – something which is easier to deal with than a centralized controlled environment. Apart from these, the platform is designed to be easy to scale, troubleshoot, monitor and optimize.
Peeking into a data platform without SDX
None of the other data platforms have SDX, and this makes Cloudera stand apart. But is SDX even needed? Is it a necessity? How does a platform without an SDX look actually?
One word to describe them would be expensive, but let’s have a deeper look into it.
Specialist providers were popular before SDX came into the picture. Here, a customer has to buy each application or platform separately from discrete vendors. Then, the customer needs to build a team of developers to work on each platform separately, and eventually ask them to stitch it all together into one single application.
Portfolio providers are another alternative in the market, who sell all platforms at a single place, but separately. Once they’ve bought all they need, the customers need to hire a large team of developers to work and assemble it all.
Then comes the Hadoop pure-play providers. Hadoop provides a distributed file system, hence HPP providers sell single platforms capable of data sharing. However, the nature of the data shared is raw. When it comes to content sharing, as in governance or security protocols, the customer needs to bridge the gap on their own with no data sharing support from the platform’s end.
It is self-explanatory that SDX makes these functions much cheaper, easier, faster and the most optimum option available in the market when it comes to data sharing and big data analysis.
SDX in the cloud
SDX is increasingly difficult to achieve in a cloud environment. This is because every application on the cloud runs on an isolated environment or a separate VM. Without SDX, each workload degenerates into private silos which means more work for the developer team. With SDX, the users work in a single cloud cluster which makes data sharing possible and allows each workload to take advantage of the shared resources. The storage layer, the metadata layer, the compute layer, the management layer, and the user interface layer work together in an IaaS (Infrastructure as a Service) to make SDX work in the cloud.
A Big Data success needs experts who can demonstrate their expertise with the tools and methods of the Hadoop stack. The Cloudera Big Data Analytics provides a focal scalable, adaptable, secure atmosphere for managing workloads from cluster, bilateral, to real-time analytics. It is a single platform solution for Big Data operations scaling as needed to support more outstanding burdens, more clients, and more information across all locations.
The launch of Cloudera SDX in the cloud environment has helped software developers all over the world and has broken down barriers that were inevitable at a point of time. Is it any wonder why it continues to trend?