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Big data tools for midcaps and others
Arithmagician
Every company, from a small online store to a multinational group, collects detailed data about daily events. The events range from purchase transactions, through the effect of marketing initiatives, to the company's social media activities. This huge volume of unstructured data – big data – promises to deliver valuable insights and abundant material for decision making. If you want to benefit from this information, you have to face the challenge of big data.
SQL, NoSQL, Hadoop
Conventional big data solutions still lug around the legacy of an ecosystem built around a database – whether SQL or NoSQL. Astronomical licensing costs take them virtually out of the reach of medium-sized enterprises, especially if high-availability features are desired.
The biggest bottleneck in these solutions is often the database, because it typically will only scale beyond the boundaries of individual servers with considerable administrative overhead. Conventional data analysis methods and relational databases can reach their limits. Even some cloud solutions do not scale without mandatory downtime. One possible way out of this dilemma is Hadoop [1].
Apache Hadoop is a framework for distributed processing of, in particular, unstructured data on computer clusters. Hadoop makes it possible to run computational processes at low cost, whether on-premises on commodity hardware, at a data center, or in the virtualized environment of a cloud service provider.
Hadoop Features
Access to an ordinary relational database relies on queries in one of the many dialects of SQL (Structured Query Language). If you need to access non-relational databases, other languages besides SQL are possible (hence the term NoSQL). Hadoop does not fall into these two categories, because it simply does not use
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