Through generations, the idea of intelligent machines helping humans in everyday life kept us looking with high hopes into tomorrow. The advent of Big Data has let us look with great expectations right into today.
Big data has been in the spotlight for several years already, drawing probably as much attention in the business world as the dark matter does in the scientific community. Similarly to the dark matter, big data is an all-pervasive substance that has been around since the zero hour - and now we can finally capture and decypher it. This is nothing short of both a scientific and business revolution.
As a result, the Business Intelligence software is undergoing a revival of popularity by exploiting computational techniques of a totally different calibre - presented by Big Data analytics, they are as more potent than traditional BI as the Hubble Space Telescope is in comparison to a home scope. This is the type of analytics we are focused on at CyberVision.
CyberVision is one of the few companies out in the Big Data field who can demonstrate a broad portfolio of accomplished solutions that truly deliver on the promise of the Big Data potential. With our inside-out experience with the data warehousing and analytics ecosystem and strong focus on data science, we implement big data processing systems for even the most sophisticated business needs.
Data management becomes more sophisticated when it comes to big data. Vast data volumes and business demands for near real-time analytics can no longer coexist with traditional data marts and legacy BI software. To allow for big data processing, the data management infrastructure must largely rest upon different paradigms: distributed processing, clustered file system, NoSQL distributed storage, data science, etc. The need for software/hardware infrastructure upgrade for big data analytics is also justified from the perspectives of cost-effectiveness, scalability and maintainability: open-source software, like Hadoop, requires very modest up-front investments and spares businesses the over-dependency on proprietary tools.
However, Hadoop is not a one-size-fits-all solution and some companies may be better-off using traditional data warehouses or a combination of both approaches. In the latter case, the success of Hadoop adoption comes down to how seamless an initial integration between different data management systems has been performed.
SQL, NoSQL, in-memory, distributed databases
Hadoop 1/2 (HDFS, Sqoop, Flume, MR, Pig, Hive, Impala, Mahout)
Virtualization: OpenStack, AWS, CloudStack, Yarn, Mesos, LXC, Ltmctfy,KVM, Xen, Hyper-V
Scalable distributed computing: Tez, Spark, Kafka, Akka, Storm, Avro, Thrift
Recent advancements in microprocessor and IoT fields have simplified and cheapened the process of big data collection. Now it is possible to use a broad variety of endpoints which could be also made remotely configurable by the means of the IoT software. What is more, endpoints themselves can process data and send out aggregated reports, as opposed to the classic model where entire raw data generated by endpoints is analyzed at the data centre. Known as the edge analytics approach, it can reduce the data load on analytics servers by hundreds of times.
IoT-powered data collection
Data science is the heartbeat of modern analytics and, thus, an essential part of big data focused projects. To extract actionable insights from Hadoop data lakes, it is incumbent upon organizations to implement appropriate data analysis models by reconciling a given business strategy with the relevant mathematical approaches and applicable big data technologies. Such a non-trivial task requires both in-depth knowledge of scientific methodology and hands-on experience with common business cases.
Data lakes design and data wrangling
Data analytics for wearables and welfare devices
Predictive analytics, machine learning applications
Given its ubiquitous nature, big data contains a limitless potential for every line of business. Contact us to learn more about the practical value your enterprise can obtain from a well-adjusted big data analytics system.