Real time processing, Elastic Architecture, High
Discover Real Power of Big Data
Analytics With BDB Pipeline
Let the organized data lead
your business strategy in an efficient way. Gain seamless
insight into huge amount of structured, semi-structured or
un-structured data ranging from UI activities, logs, performance
events, sensor data, emails, social media to organizational
documents. Support your decisions with an advanced machine
learning algorithms and visualization techniques all in one go.
Enterprises dealing with
multiple sources of business data may have information
collected from different ERPs, diverse applications, social
media (structured, semi-structured, and un-structured data). If they go by redundancy of processing their data, they need to move the entire volume into batches via ETL layers written with different products, stored procedures, triggers
etc. Ultimately, they will end up in complexity without a
Data Ingestion Layer
Big data ingestion is about
moving data from the Existing Systems or where it originated,
into a system where it can be stored, processed, and analysed.
BBDP moves the collated data into a massive Data Lake (such as
Hadoop HDFS based, Cassandra, HBase etc.)
Data ingestion may be
continuous or asynchronous, real-time or batched or both (lambda
architecture) depending upon the characteristics of the source
and the destination. In many scenarios, the source and the
destination may not have the same data format and will require
some type of transformation to be usable by the
destination system. BDB Data Pipeline harnesses the potential of
Apache Kafka integrated with Apache Spark for supporting this
Our API gateway built on top
of the BDB Data ingestion layer facilitates data lake software
neutrality. The coarse-grained API abstract users from the
nitty-gritty details of low-level data interaction. Customers
can use our API set to ingest data into the data lake layer.
Note: In this case, the
customer should follow certain rules, which our system cookbook
Data Lake is a repository that holds a large amount of raw data in its native
format until it is needed. While a hierarchical data warehouse
stores data in files or folders, a data lake uses flat architecture to store data. Each data element in a lake is
assigned a unique identifier and tagged with a set of extended
metadata tags. With the rise in business, the data lake can be
queried for relevant data, and that smaller set of data can
then be analysed to help answer the question.
BDB Data Lake can be either
a Cassandra or HBase or Hadoop HDFS Parquet depending upon
what our customer chooses with Apache Spark alongside as the processing engine.
BDB Big Data technologies
allow large amount of data in an extremely operative, yet cost friendly manner by keeping many zones in a data lake.
Raw Zone – This zone
contains the transactional data which has been ingested, it
contains the data ‘as is’ before any Data Cleaning or
Error Zone –This zone
contains disputed data that failed to pass the data quality
checks or remains unclear after passing the quality and
clean-up processes. The data analysts will re-work on this
data to clean it up and move into the trusted zone. Customers
are provided with an API layer to avoid nitty-gritty of the
Spark + Persistent layer.
Trusted Zone – This zone
will have all the cleaned data ready for analytics purpose.
The Trusted Zone becomes the Analytics store.
The data preparation layer
takes care of the data transformation and cleansing activities.
The spark computation set feature included isn’t limited to Spark SQL and Spark ML pipeline in addition to our supported set.
Our Scala script is accountable for provisional support to custom transformations.
Our BDB ETL tool provides an
extensive toolset for data transformation and cleansing.
The Metadata layer contains
all the mapping rules and data attributes about the data input
and data output workflow.
The Engine can read data from
the data Lake through API and it can perform all the required transformations and calculations which are necessary for storing the desired result in a computer storage or an analytical storage.
Our robust MDS UI facilitates
to create Compute (Data Pipeline) workflows and schedule it on
top of a Data Lake. All the MDS governance can be managed
through the MDS UI.
The BDB Analytics Layer
follows a data mart or data store-centric approach to a set of
business metrics. After the data preparation operation, the
trusted data from the Data Lake can be
aggregated, calculated, or filtered to suit a narrower business
need into an Elastic Search Store or a Cassandra Store using
the Spark Computation logic and Spark SQL. These analytic data
stores can be used for reporting, dashboarding, or analytical
purpose via our visualisation tool. Customers are provided
with an API set to exchange data easily avoiding the
complexity of the analytics layer.
Note: The data Lake also has
an API set for its interactions.
BDB’s Advanced Analytics
capabilities spanning ad-hoc statistical analysis, predictive
modeling, real-time scoring, machine learning, elastic search
and much more. It helps organizations discover patterns and
trends in structured and unstructured data to go beyond so they can go beyond
knowing what has happened to anticipate what is likely to happen
next. BDB platform has very strong Predictive Analytics product.
The Predictive tool has basic transformation capabilities and it
integrates with R & Spark ML. One can write Scripts in R, Spark
ML Scala & Python to create the models as desired by the
businesses to find new opportunities, reduce risks, and increase
revenue. Python is used to create the computed view as desired by the
businesses to find new opportunities, reduce risks and increase
BDB data visualization is
the presentation of data in a pictorial or graphical format.
It enables the decision makers to visually experience the analytics presented to grasp difficult concepts, meanwhile identifying new patterns. With interactive visualization, you can take the
concept a step further by using technology to drill down into
charts and graphs for more detail, interactively changing what
data you see and how it’s processed. BDB provides all forms of
Data Visualisation (Reports, Dashboards, Advanced Analytics &
Self-Service BI) that covers every stakeholder in an
BDB Pipeline Features
All Integrated in One
The BDB system works in one
platform/integrated ecosystem on Cloud rather than working in
SILOS like other vendors.
Reduces deployment time
with Multiple Products
Big Data Pipeline, ETL, Query Services, Predictive Tool
with Machine Learning, Survey Platform, Self Service BI,
Advanced Visualization and Dashboards provides everything in
Customer Approval for
When BDB was compared to several marquee
brands, the buyers have admitted that they would have taken
about several years to deploy a BI solution, BDB did it in a
A customer can scale from a few 100 users
to a 100mm in near real time- Such business solutions can be
made available from SaaS to a White Label.
Create & Sell your own
Subscription Services & Licenses
Instead of selling third party licensing
package, a customer can sell their own Subscription Services
and Licenses – Market opportunity 10x of investment Additional
Analytics Services revenue.