Today the massive volume of data produced by various electronic devices in conjunction with technological improvements regarding storage, processing & analyzing has allowed big data analysts and business users to make better and faster decisions using data that was previously inaccessible or unusable.
The availability of mobile devices, environmental sensors, software and application logs, cameras, RFID detection devices, wireless networks, weather stations, radio frequency systems, online social networks, internet documents and articles, health, medical and data related patient, data store management systems has allowed for the creation of data in massive quantities, moving from gigabytes & terabytes to petabytes, exabytes and zeta bytes, which needs to be collected, stored, searched and made available for analysis.
Achieving proficiency and mastering Big Data capabilities allows us to be better productive, improve situations & respond to security threats by providing better security analysis and correctly predicting future trends.
In a world where quantities of bank, insurance & financial transactions are increasing, decreasing fraud perfectly demonstrates best use cases for Big Data technologies.
Big Data presents many challenges, initially, volume of data, data production rates & data diversity challenges were presented to us while working within the realms of Big Data; however, the following additional challenges were also encountered by our teams:
- (Volume) Data Quantity: Internal and external organizational data is increasing at astonishing rates and it has been predicted that by 2020, 10 Zetabytes of data would have been produced throughout the world.
- (Velocity) Data Production Rates: Using operational programs and various sensors which exist within the environments around us, it is now possible to produce data at very fast rates which needs to be stored and processed as well.
- (Variety) of data sources and variety in the types of data that exist today due to structured and unstructured data is very varied. Some data is stored in databases, some as XML, JSON as well as other formats which further complicates the processing of this data.
- (Veracity) of data, even though data is received from various sources it might not be possible to easily trust all data. An issue which cannot be overlooked in Big Data ecosystems.
- (Validity) of data even if presumed to be accurate might still not be valid for certain use cases and situations.
- (Volatility) of data and speed of assessing the worth of data might be different in various situations. Even though storing data over time in order to properly analyze data is of importance, this can be costly and needs to be considered.
- (Visualization) of data is one of the more challenging aspects of Big Data. In order to properly analyze, understand and study complex situations and relationships of massive amounts of data, proper data visualization needs to be put in place and made available to the end users.
- (Value) of data is also of importance, is it worth the cost to store and process data from a decision making viewpoint.
Systematic analytics of Big Data is one of our major work areas within the last few years. Using of such systems helpsto achieve real-time processing of complex data.
Technologies which were used in previous projects was as below but not limited to the these:
- Apache Spark
- Apache Hadoop
- Apache Kafka
- Apache NiFi
- Apache Impala
- Elasticsearch
- Docker
- MongoDB
- Apache Cassandra
- Apache Flink
Advantages:
- What differentiates us from competitors is our deep knowledge and experience in working with live data streams in volumes of more than 6 billion records and 2 petabytes of data.
- Use of cutting edge technologies in management and analysis of Big Data ecosystems.
- Real experience in management & development of Big Data ecosystems in the financial and telecommunication industries.
- Major improvements in achieving commercial and service supremacy.
- Designing scalable environments.
