Information Management

Data consolidation into single instance AtomicDB Data Warehouse


  • Data warehouse may be a relatively mature concept, but the core technology is still decades old and innovating rapidly.
  • The traditional databases have begun to creak under the strain of volume, velocity, variety and veracity of data being generated in today's world.
  • Big-Data Implementations such as Cloudera, Hortonworks and MapR are gaining traction, but the truth is – it is complementary to relational database (RDBMS) not a replacement.
  • In-memory approaches to data warehouse is faster and becoming cheaper but it has its own challenges and limitations.
  • Inability to house all the data within a single database had led to databases in silos, which is being brought back into a data warehouse. This is a time consuming, expensive, error prone and not suitable for current or near real-time data.

  • 'Associative database technology' is the only technology that can address most of today’s challenges.


  • Simplify, manage and implement big data solutions
  • Reduce energy consumption due to reduction in Technology Infrastructure needs
  • Consolidate multiple data warehouse into a single instance
  • Easily and accurately aggregate and correlate data
  • Address data security and privacy concerns
  • No query language, only 7 API calls are needed for perform data read and write operations
  • Significantly reduce Application development cost


Bad Data Quality

Relational Database paradigm leads to the following data issues and challenges –

  • Duplicate Data, NULL Data, Data type constraints, Mandatory constraints, Foreign key constraints, expression pattern
  • Non-standard, Incompleteness, Inaccurate, Typos, Inconsistent, data uniformity and integrity issues

Expensive Cleansing Process

As data is extracted from various redundant systems it requires extensive cleansing, which leads to –

  • Heavy auditing process and screening
  • Extensive ETL (Extract, Transform and Load) process specification, development and execution
  • Post processing reviews, controls and validations
  • Heavy resource investment in data cleansing

Expensive Reconciliation Process

Extensive ETL process requires extensive reconciliation, which leads to –

  • Loss of data, misinterpretation, oversight, incorrect calculation
  • Very time consuming and expensive to updated corrected data back to input sources
  • Auditing and explanation of variation in reports

Disparate OLTP and OLAP Data Repository

Current data warehouses is not suitable for Transactional and Analytical processing at the same time, leads to –

  • Maintaining separate data repository for near real-time Transactional systems, Reporting systems and Analytical systems
  • Complex and expensive implementation are required to aggregate and correlate data


Data Quality Maintained at Source

Advantages with AtomicDB –

  • Data type agnostic, Single instance value per column, Data de-duplicated on load
  • Gold copy of data maintained as single source, so completeness, accuracy, consistency and integrity are all addressed at source once

Minimal Cleansing Process

Since data resides in a single instance storage -

  • Clean data is maintained at the source in one place
  • ETL (Extract, Transform and Load) process are not required. Directly Extract and Load, transform during consumption.
  • Eliminates post ETL process overheads
  • Significant saving by minimizing the cleaning process

Minimal Reconciliation Process

Since ETL (Transform) process are not required –

  • No Loss of data, misinterpretation, oversight, incorrect calculation
  • All reports and data are based on gold copy of data in the source
  • Auditing and explanation is only required during data modification

Single instance for OLTP & OLAP

Being Atomic in nature, the architecture natively supports –

  • Transactional systems and Analytical systems against the same single instance data source
  • Correlation of data is done at the source by association, so there is no need to look for references, it is always available


Information Management Service Overview

Atomic Information Management Service is a (SaaS) based model, which allows customers to upload their data and use the capabilities of AtomicDB to cleanse, consolidate and aggregate their data.

If you are interested in learning more about the service, click here to get in touch with us.

Conventional Data cleansing Process Life Cycle

Data cleansing or data cleaning is the process of detecting and correcting (or removing) corrupt or inaccurate records from a record set, table, or database and refers to identifying incomplete, incorrect, inaccurate or irrelevant parts of the data and then replacing, modifying, or deleting the dirty or coarse data.

For those who have been dealing with this process, would agree this is no trivial task. The above process flow diagram is a conventional approach followed and each stage is painful and time-consuming process. Any optimization and efficiency would mean significant cost saving.

Our Data cleansing Process Life Cycle

AtomicDB is based on Associative technology where every data element is an object stored just once (single instance storage), which means only one unique value per item is stored in the database. As part of the initial load process, the data is deduplicated and all NULL values are eliminated.

Data consolidation and Aggregation

A true object oriented design approach will facilitate reuse of information without having a need to ever duplicate them.

Breaking down the silos by creating a single instance data warehouse or custom data collections allowing cross integration of organizations and systems.

Driving sustainable cost reduction from infrastructure optimization, simplification, operational efficiency and reduction in time and human resources.

Co-Existence of Disparate and ADB Systems

We provide a phased out approach, which allows customers to configure and setup Bi-directional data bridges, which establishes two-way connection between current systems and AtomicDB Data Warehouse.

Existing applications can continue to work against the current database systems, while new Business Intelligence, Reporting and Applications can be designed on newly established AtomicDB Data Warehouse.

As efficient newer applications are developed, implemented against the new AtomicDB Data Warehouse, old Application can be decommissioned gradually.

This approach allows for seamless transition and minimum system downtime, which translates to 'No Business Risk'.

Demonstration of Data Aggregation from multiple sources into AtomicDB

If you are interested in a demo or learning more about the service, use the information below to get in touch with us.


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