techbytes

While HANA is SAP's answer for Big Data,then it is interesting to see how does HANA fits into fits into Big data paradigm. In particular, we will talk about HADOOP stack of technologies in view of Big Data. Welcome to world of unstructured data and HADOOP is ideal for processing large data sets & batch processing where response time is not a concern. Here Schema is not a concern as we are dealing with unstructured data and its not  optimized for data updates/inserts. It works with principle of write once, read always. Also random reads are not supported and data can only be appended. Having said above things, HANA follows for rows/columns storage model and we have to define schema before storing data. HANA is In-memory appliance thus supports faster read times/ on-the-fly aggregates/joins. After highlighting merits of both worlds, we can see that both comes from different worlds; HANA from world of structured with fixed schema optimized for real time access , HADOOP from...

Before we start, welcome to unstructured data. We will quickly review where current relational databases are lagging and need of new generation No-SQL databases. Examples are MongoDB, Cassandra, HBASE.                                                      Relational Databases are designed in times when memory costs were high and disk seek times are high. So the design of  database was focused on optimizing the memory required for string data. Hence  Normalization  of data model was promoted(No data redundancy). Normalized data model is also optimized for Inserts/Updates. Although Index are supported for sake of faster access,  primary concern was saving the space. However, we currently live in a world where memory costs are so cheap such that faster reads, Real-time systems are the need of hour. In current situations, we can comprom...