Big Data Solutions

Big Data Systems and Applications to Boost Your Business Success

What is Big Data?

Big Data is a term applied to data sets and technology stacks that exceed the processing capacity of traditional software tools. In most cases it means that data volumes, formats and sources don't allow to effectively capture, store, request and analyze the data in relational databases within a required elapsed time. Here are some symptoms that your current data technology, architecture or strategy fits into the Big Data category

  • Frequent write operations lock data records and block reading operations. An increasing data volume affects retrieval operation timing so that a search or extraction of business data can't be completed within appropriate time limits;
  • Your IT guy says that adding new fields to the table will require more than a month of testing as it affects all the systems involved in the data table and requires changes in data models;
  • You have to procure new hardware with a more powerful CPU, hundreds of gigabytes of memory to process your data in time.

DataArt Big Data Solutions

If you see any of these symptoms you’re certainly dealing with Big Data. In that case, Big Data Technologies might help you.

Big Data technology stacks allow to effectively capture, store, select and process data of big volume, variety and velocity. These technologies were invented by internet giants such as Yahoo, Google and Facebook because they first dealt with unstructured data on a large scale. Several key terms and principals are the backbones of the Big Data technologies

NoSQL Systems

  • Document-oriented
  • Series of key-value pairs is an equivalent of column/rows in relational databases
  • Structure of data record is not predefined. It allows to add/remove "columns" with no penalty and gives architects more flexibility and fewer limitations
  • MongoDB, CouchDB, Cassandra, Redis, BigTable, HBase, Hypertable, Voldemort, Ryak and others

Map Reduce

  • Algorithm Design Pattern
  • On the "Map" stage of the algorithm, the programming task divides into several sub tasks with the ensuing sub-task distribution closer to data location
  • On a "Reduce" stage, the results from the sub-tasks are combined into result value

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Key-Value Storages (including in-memory caches)

  • Simple interface
  • Predictable performance
  • Effective building block of any system
  • MemCache, Redis at some extent, Oracle Coherence, Gemstone, Gigaspaces, NCache, Teracotta and others

Horizontal Scaling

  • System running effectively on a cluster of cheap low-spec servers
  • Scaling is simple: adding more low-spec servers allows to process lager volume of operations
  • Writing speed/data consistency dilemma
  • Achieved mostly due to MapReduce algorithm implementation
  • Hadoop, GridGain, Hazelcast

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