In-Database Processing and In-Memory Analytics

  • Pethuru Raj
  • Anupama Raman
  • Dhivya Nagaraj
  • Siddhartha Duggirala
Part of the Computer Communications and Networks book series (CCN)


With the ever increasing data and high dependence of digital services for day-to-day jobs, there is a huge opportunity for enterprises and huge risk for security of users. This is why organizations and governments are in dire need to analyze and understand data. Sometimes analyzing data is not just enough, but the speed of arrival at that insight is also extremely important. This is where the in-database processing and in-memory analytics come into picture. Data is huge in size making it suboptimal to move data from corporate SANs to processing servers. Instead of moving the data, moving the processing to the data is the principle advocated by in-database processing, while the in memory focuses on keeping the data completely in memory to increase the processing speed. In this chapter, we will learn and analyze these two and study some use case to improve our understanding.


Performance Performance Data Warehouse Complex Event Processing NoSQL Database Analytical Appliance 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Chaudhuri S, Dayal U (1997) An overview of data warehousing and OLAP technology. ACM Sigmod Rec 26(1):65–74CrossRefGoogle Scholar
  2. 2.
    Inmon WH (2005) Building the data warehouse. Wiley, New YorkGoogle Scholar
  3. 3.
    Chen H, Chiang RH, Storey VC (2012) Business intelligence and analytics: from big data to big impact. MIS Q 36(4):1165–1188Google Scholar
  4. 4.
    Russom P (2011) Big data analytics. TDWI Best Practices Report, Fourth QuarterGoogle Scholar
  5. 5.
    Taylor J (2013) In-database analyticsGoogle Scholar
  6. 6.
    Nes SIFGN, Kersten SMSMM (2012) Monet DB: two decades of research in column-oriented database architectures. Data Eng 40Google Scholar
  7. 7.
    Chaudhuri S, Dayal U, Narasayya V (2011) An overview of BI technology. Commun ACM 54(8):88–98CrossRefGoogle Scholar
  8. 8.
    Teradata. (n.d.) Retrieved January 5, 2015, from
  9. 9.
    Dinsmore T (2012). Leverage the in-database capabilities of analytic software. Retrieved September 5, 2015, from
  10. 10.
    Inchiosa M (2015) In-database analytics deep dive with teradata and revolution. Lecture conducted from Revolution Analytics, SAP Software & Solutions|Technology & Business Applications. Retrieved January 5, 2015Google Scholar
  11. 11.
    Plattner H, Zeier A (2012) In-memory data management: technology and applications. Springer Science & Business Media, BerlinCrossRefGoogle Scholar
  12. 12.
    Plattner H (2009) A common database approach for OLTP and OLAP using an in-memory column database. In: Proceedings of the 2009 ACM SIGMOD international conference on management of data. ACM, New York, pp 1–2Google Scholar
  13. 13.
    Acker O, Gröne F, Blockus A, Bange C (2011) In-memory analytics–strategies for real-time CRM. J Database Market Cust Strateg Manage 18(2):129–136CrossRefGoogle Scholar
  14. 14.
    Färber F, May N, Lehner W, Große P, Müller I, Rauhe H, Dees J (2012) The HANA database–an architecture overview. IEEE Data Eng Bull 35(1):28–33Google Scholar
  15. 15.
    SAP Software & Solutions | Technology & Business Applications. Retrieved January 5, 2015Google Scholar
  16. 16.
    Raman V, Attaluri G, Barber R, Chainani N, Kalmuk D, KulandaiSamy V, Lightstone S, Liu S, Schiefer B, Sharpe D, Storm A, Zhang L (2013) DB2 with BLU acceleration: so much more than just a column store. Proceedings of the VLDB Endowment 6(11):1080–1091CrossRefGoogle Scholar
  17. 17.
    Konitio. Retrieved January 5, 2015.Google Scholar
  18. 18.
    Francisco P (2011) The Netezza data appliance architecture: a platform for high performance data warehousing and analytics. IBM RedbooksGoogle Scholar
  19. 19.
    GLIGOR G, Teodoru S (2011) Oracle Exalytics: engineered for speed-of-thought analytics. Database Syst J 2(4):3–8Google Scholar

Further Reading

  1. Kimball R, Ross M (2002) The data warehouse toolkit: the complete guide to dimensional modelling. Wiley, [Nachdr]. New York [ua]Google Scholar
  2. Nowak RM (2014) Polyglot programming in applications used for genetic data analysis. BioMed Research International, 2014Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Pethuru Raj
    • 1
  • Anupama Raman
    • 1
  • Dhivya Nagaraj
    • 1
  • Siddhartha Duggirala
    • 2
  1. 1.IBM IndiaBangaloreIndia
  2. 2.Indian Institute of TechnologyIndoreIndia

Personalised recommendations