Create big data analytics services and release them quickly and reliably
Hydrosphere.io increases the ROI of big data platforms. It does this by letting data science expose analytics as services. That provides web and mobile products end users need to make data driven decisions. This approach is an order of magnitude better than the DW model of handing out static reports or clunky spreadsheets.
Hydrosphere.io opensource products and hosted services also bring the devops process and efficiency to data analytics development.
This Analytics as a Service and devops approach drives the adoption of data driven applications and makes possible the quick and reliable release of data science algorithms.
Why business should move from Hadoop based data warehousing towards analytics services and products?
It’s time to kill off and bury forever the old model where the DW data analyst generates a report and throws it over the wall to the business.
The advent of big data software and its many frameworks, tools and procedures let internal and external users, tenants, and B2B customers tap into all of that easier. It drags data science out of its silo and makes it available to those who need it and provides it in a self-service fashion.
From offline data warehouse
To online analytics products
Big Data and the elusive goal of positive ROI
As businesses see their competitors and other businesses rushing into big data, they also kicked into high gear, investing huge sums of money in Hadoop clusters, big data databases, and ETL platforms and hiring teams of data scientists.
They did all of this in the hope of realizing a positive return on investment. But to reach that sometimes elusive goal requires a change in approach due the complexity and novel nature of big data.
Business is slowly coming to realize that static offline data and algorithms and isolated data warehouses are without much value. What gives data value is its application to business in real time.
But to achieve this, the process by which data is captured and consumed by engineers and scientists must be tightly integrated with business planners. Algorithms developed by data scientists need to be exposed online so that programmers and users across the enterprise can tap into that.
That’s what Hydrosphere.io does. It does this by pushing the technology ahead another crucial step.
Filling the gaps with Hydrosphere.io
Bringing sanity to the cycle of trying to tie together data scientists, engineers, operations and business.
Hydrosphere Swirl and Notebooks bridge the gap between data scientists and operations. They eliminate the disjointed and often broken handoff between data scientists and big data engineers.
They also help data scientists understand how traditional IT works. Data scientists need to have at least some understanding of IT so that they will know what is possible and practical.
Another tool in the Hydrosphere.io arsenal is Mist . It provides unified middleware between data science algorithms and end user application engineering. That makes it possible for business to use analytics as services by letting developers expose those as user-friendly web or mobile applications.
These tools together foster a devops culture where end-to-end teams drive their own innovation while specialist functional teams provide high-performance “lego bricks” to be consumed by other teams.
Release data analytics services quickly and reliably
Everyone talks about how important it is to speed products and ideas to market. That is true. You cannot waste time fumbling around with disparate tools and clunky manual processes trying to get the new ideas into a working state.
This is where you need to fit a devops-driven, Jenkins, Ansible, and Docker Continuous Release approach onto data science. Traditional programming has adopted those practices because they have been shown to smooth all this out.
Hydrosphere.io plugs data science and data engineering into this software development model and Agile methodology, where coding, testing, and deployment are all run as automated processes and every abstraction is coded.
This helps business speed products to market.
Keep the lights on and engines humming
Bringing all of this together is where Hydrosphere Hosted helps. With its set of tools and Continuous Analytics processes we keep the lights on and the machines running down in the big data engine room. That makes all of this infrastructure available all of the time. That is the key difference with the staid and static DW.
We also bubble up analytics and big data services and bring them forward with simple abstractions that let ordinary programmers and end users tap into the complexities of that. And keeping with the Continuous Analytics principle, we use a feedback loop to improve these models with each iteration.