BIG DATA

START SMART – GET BIG: BIG DATA MIT VIRTUAL7

Today, drawing qualifi ed conclusions from production, research or customer data brings many companies crucial market advantages. But in order to correctly understand and evaluate the dropped and collected data, one needs solutions and concepts that have to be developed specifi cally for one company. Generalized standard approaches are often insufficient. On the basis of existing ORACLE products, we create a big data solution for you that can bring you an enormous added value. For us, your questions and analysis requirements form the basis for the joint development of use cases, the creation of a concept and to check what your data gives away and how smart the results can look. We help with.

  • Defining the architecture and selecting the technologies
  • Calculating the business cases for the introduction of big data
  • Implementing the big data solution
  • Integrating the interfaces
  • Sizing the solution
  • Structuring data lakes
  • Analyzing the data (analytical services)
big_data_virtual7

V FOR VOLUME, VELOCITY, VERACITY

The challenges for big data are becoming more diverse. In the initial perception, big data was the answer to the problem of the increasing data volume, mainly in relation to data keeping and batch processing. On top of the familiar challenge known as volume problem, there were the challenges of data processing velocity, different data formats veracity and more.

ARCHITECTURE

There are many possibilities when developing big data architecture. Currently, the lambda and kappa architectures are often a subject of discussion. They try to answer to different challenges for the reaction times with a uniform approach. Here the matching approach is to be selected, future-proof, but not unnecessarily complex.

INTEGRATION

A detached big data initiative will not achieve the success of one that integrates the existing systems. For this, the big data solution can store data as well as provide it. The ORACLE environment is especially exciting in interaction with a classic data warehouse. With the ORACLE data integrator and the connectors of the database, professional tools are available that make the integration between data in the big data architecture and the data warehouse simple and fast. The possibilities when using the ORACLE engineered systems are even more diverse.

GROWING WITH THE CHALLENGES

Some of the requirements for big data solutions can be met with simple architectures and few tools. With time and past experiences, the systems can become more complex without having to produce excessive initial costs.

In the beginning, an infrastructure with few knots and open source solutions can be used. With an increasing number of requirements, it can grow to a mature data lake by using big data appliances.

CREATE KNOWLEDGE, CREATE VALUE

Simply storing a lot of historical data only makes sense to a certain degree. A big data solution only becomes valuable by showing new contexts and finding new business strategies. Of course, there is also a potential for cost saving, e.g. through lower storage costs. Most potential is usually gained by using data mining, machine learnings and other analysis methods.

EXADATA AND MORE

Even in the Exa you do not have any more storage space for historical data or no computing time for complex calculations? Use e.g. Sqoop to integrate the data between Hadoop cluster and database. Of course, you can also use other open source products. Ask us!

OUR SERVICE OVERVIEW: