In-database Analytics Opportunities & Challenges: Crucial Insights from Remote DBA Expert

In-database analytics seems to be an emerging trend that promises to cut costs and enhance the performance of all analytical processes. Here we would be discussing some basics of In-database technology. In-database analytics actually refers to a distinctive model of analysis that involves data processing within a database for eliminating the overhead associated with moving sets of large data to analytic applications.

In this sort of a model, we find that the analytic logic seems to be built within a database rather than a separate application. The benefits of In-database analytics would include scalability, parallel processing, partitioning, and analytical optimization. In case of the In-database analytics, we find that the analytical functions are actually present in the database. Hence, there is no need for the queries to keep waiting while the data is being loaded.

The In-database analytics system is known to comprise an effective enterprise data warehouse actually integrated seamlessly with the analytic database platform. You must be aware that In-database analytics could be used particularly in applications requiring intensive processing. This is chiefly because the data marts and the datasets are consolidated effectively in the data warehouse system of the enterprise that would be facilitating and securing data analysis, retrieval, and even processing.

Advantages of In-Database Analytics

  • It boosts predictive analytics capability of an organization.
  • Effectively identifies future business opportunities, operational analytics, risks, area trends, and all issues associated with the exchange of information and data.
  • Efficient at providing ad hoc analysis reports, that allows chief users to generate new reports and effectively have easy access to details like transactions and records.

The Present Challenge: Handling Excessive Data

A few instances would be demonstrating the real magnitude of data challenge in current times. Suppose an average-sized healthcare insurance provider thinks of improving the results for patients suffering from diabetes, they would require analyzing over 60,000 medical codes spanning across 10 billion claims. They would also require factoring a completely separate pharmacy data silo into the equation. This kind of a challenge seems to be equally daunting in some other industries as well. A nationally-operated retail chain that thinks about improving its product replenishment would require examining an incredible amount of sales data stored in thousands of different SKUs or stock keeping units throughout thousands of stores during the past few years which amounts to over 100 billion rows full of data.

For many years now, data analysis was regarded as literally a moving experience. Organizations have been moving the data which they wished to analyze to analytic servers from their database for breaking the massive analytic work into manageable and smaller pieces. Several organizations are doing this even today. However, there are several issues associated with this custom. As data becomes bigger, it would be taking hours for transferring and staging the data on numerous servers and effectively return the data to the original database and then re-assemble the whole thing. This could be a major drawback for any sort of time-sensitive analysis. So organizations are today cleverly analyzing just a subset of all their data. However, this kind of data sampling may be culminating in erratic analytical models and ultimately, would end up generating more data confusion simply by creating multiple versions.

Move the Analytics, Not the Data

To find perfect solutions to all these current Big Data challenges, organizations are using a brand new strategy that is, In-database analytics. The In-database analysis is all about moving the analytics but definitely not the data. Simply bring the analytics engine directly into your database and then leveraging hugely parallel map-reduce technology, organizations are capable of performing highly intricate analyses within the database environment.

You must realize that In-database analytics would be yielding a number of benefits as compared to conventional analytics including:

  • Stricter and more robust security policy enforcement, especially, for industries regulating the sensitive business data movement.
  • Truly pervasive analytics which could be flowing freely to applications and reporting tools.
  • There would be no data duplication issues because of movement of data between servers.
  • Almost close to real-time insights as compared to analytics insights which could be a few weeks or a few days old.
  • Much faster analytics.
  • Capex reduction simply by getting rid of the requirement for more hardware servers for processing the analytics.
  • Definitely better analytic models because data scientists could be using full datasets as opposed to sampling.

Data Science Isn’t All that Hard!

?Data has grown and evolved by leaps and bounds over the last couple of decades, and so has the role of the data scientist. Rumors decree that the ideal data scientist possesses a knowledge of statistics and machine learning that rivals God himself, is able to program in low and high-level languages, and is an expert in data processing and visualization, able to see patterns where they are barely recognizable, and predict the future. These unicorns are hard to find, and cost a considerable amount to employ.

Even beyond these issues, putting them on a pedestal isolates innovation. Instead, you should opt for In-database analytics which reduces the complexity manifold such that your real-life, run-of-the-mill data analysts can use familiar SQL queries to perform analytical tasks. This approach allows everyone to contribute to data-driven discoveries and also demystifies the role of the analyst, thus making it easier for the company to attract talent. You may seek the services of for perfect solutions.

Where Does It Work?

In this day and age, there is no one-stop blanket solution for working in a particular domain, especially one as diverse as data analytics. In-database analytics is a great solution but it might not be for everyone; new use cases for Big Data analysis are better-served with specialized tools. That said any business which requires quick analysis of large amounts of structured data would do well with In-database analytics. Some great options for this are:

  • Healthcare organizations which require analyzing securely mammoth amounts of data relating to patients.
  • Financial services agencies that would get a competitive edge thanks to real-time decisions in critical investment strategies.
  • Some reputed retail corporations which require improving supply chain logistics. They would also benefit from the In-database analysis in terms of analyzing product performance predominantly in a truly dynamic environment.


In-database analytics allows companies to analyze large amounts of data to gain actionable insights to drive business decisions like never before. They have access to deep analyses, summaries, and drill-downs that were not possible with any prior technologies at this scale. All this amounts to a solid competitive advantage in situations when you save time to come to a decision. In-database analytics would be getting your business tremendous profits.

Author Bio: Ariya Stark is heading a reputed remote DBA services firm. She is a passionate blogger and loves to share her extensive knowledge with her readers. However, she recommends important resources such as

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