• LinkedIn

  • Follow via Facebook

  • Follow via Twitter

  • Submit RFP

  • Contact Us

BUILD HIGH PERFORMANCE BIG DATA LAKE WITH OUR CUSTOMER 360 DATA LAKE SOLUTION

What’s Bigdata Lake?

A data lake is a flat architecture storage system that provides massive storage for any kind of data like structured, unstructured, streaming, healthcare, financial, email, video etc. It provides enormous processing power and the ability to virtually handle limitless concurrent tasks or jobs. The data lake allows organizations to store all of their data, both structured and unstructured, in one centralized repository.

Why do you need Bigdata Lake? Frequently Asked Questions

At big data dimension, we understand that one shoe does not fit all, hence our consultants have customized solution that can fit your needs and get the maximum benefit with minimum cost or infrastructure changes, for small to medium organization we recommend the following solutions

  • To have a small cluster setup in cloud (AWS or Azure) and make use of the combination of Hadoop File system (HDFS), Sqoop, PIG and HIVE to have the structured data processing . This is a highly available, fault tolerant, scalable and robust solution with a plug and play nature and can be easily integrated with on cloud as well as offsite (on premise) existing source systems.
  • The alternative to Big Data Hadoop in case of cases where only structured data volumes are huge and need quick data processing, we recommend AWS Redshift It’s a highly available, scalable and fault tolerant multi tera byte data processing solution designed to handle and cope with structured data sets. The services are highly effective and easy to setup, the solution can be easily integrated with on cloud as well as offsite (on premise) existing source systems.

Our data engineers can assist you in making a small proof of concept on limited use case demonstrating end to end implementation to proof the gains by shifting to big data lake analytics. Reach out to our experts today, to learn more.

Data security is an understandable concern when it comes to cloud based solutions, over the period of time security aspects of data has been exponentially improved but this in some organization the business stake holder prefers to have an on premise infrastructure and setup. Our consultants are specialized in both on premises as well off-premises solutions, the alternative approach to do the proof of concept is to customize the Linux server and prepare a small cluster, integrate with source systems and utilize the native Hadoop utilities e.g. Hive, Pig, Sqoop, Hbase and Flume to put the sample use cases to test and bench mark the value gains in terms of performance and accuracy.

Our data engineers can assist you in making a small proof of concept on limited use case demonstrating end to end implementation to proof the gains by shifting to big data lake analytics. Reach out to our experts today, to learn more.

In some of the industries specially Health care there are certain compliance which must be met with IT solutions. The Cloud solutions has evolved over the period and provide very cost effective and secure solutions to meet the customer needs. Some of the reliable options are as follows in the AWS Cloud framework

  • Use a dedicated Connection line by using AWS direct connect to isolate the corporate data flow from the public internet lines and hence providing most secure data transfer from on premise source systems to the cloud environment.
  • Using the VPN Based multi factor authentication to enable very limited and secure access to the authorized personal only. More over the cloud servers are highly configurable to accept traffic only from selected Servers and ports
  • 3. To address PHI or SOX compliance, AWS also provide a dedicated host for customer which is a physically Isolated server having no physical connectivity with other Servers in the Data Centre ensuring that dedicated host servers DO NOT communicate to any other machine in the data center.

Our data engineers can assist you in making a small proof of concept on limited use case demonstrating end to end implementation to proof the gains by shifting to big data lake analytics. Reach out to our experts today, to learn more.

At Big Data dimension we believe in innovation and custom designs and prefer methodologies which will encourage customer to gain big data analytics benefits with minimum cost and effectively reusing the existing infrastructure. Our engineers are specialized and has full stack experience in the domain of DWH/BI/Data management and predictive analytics we recommend to use Big Data technologies for the cases which cannot be handled with a traditional MPP system instead of replicating the existing use cases to Big data platform. Some of the common advice are as follows

  • The storage on MPP systems is expensive hence the staging layer or archive data can be shifted to store on Hadoop file system instead of retaining it on an expensive storage.
  • Some of the complex data processing which require a lot of time or space can be shifted to a Hadoop Cluster which would make MPP system more available for Reporting and Adhoc data analysis instead of being aggressively used in the data preparation or processing.
  • Unstructured data sets (social media feed, Web logs, Network logs etc.) should be handled and processed through the Hadoop Cluster and the final product (aggregated data tables) can be copied over to the MPP system for Reporting/Dashboard analytics/ Self Service BI.

Our data engineers can assist you in making a small proof of concept on limited use case demonstrating end to end implementation to proof the gains by shifting to big data lake analytics. Reach out to our experts today, to learn more.

We have extensive experience in making 360 Degree customer analytics , to achieve this in near real time we recommend the following

  • Having a Big Data Cluster setup to ingest the data feeds from social media (e.g. Apache Flume, Apache Storm or Apache Spark or Apache Kafka) and using Sqoop or any other data integration tool to ingest data from CRM or any other source system. The processing the data using Hive/Pig or any other Data processing tool (e.g. Talend ,Informatica etc.) and final product should be stored in a NO-SQL database e.g. ( Hive , MongoDB or Casandra etc.) to have robust data access against customer analytical record.
  • Another alternative to this can be a cloud based Server less architecture by using AWS Services, the components like AWS Kenisis can ingest near real time data feeds from social media , process the information using AWS Lambda (Python,Java) or any Data integration tools ( e.g. Talend ,Informatica etc.)and store the processed data using AWS DynamoDb robust data access against customer analytical record.

Our data engineers can assist you in making a small proof of concept on limited use case demonstrating end to end implementation to proof the gains by shifting to big data lake analytics. Reach out to our experts today, to learn more.

The big data technologies are mostly open source and hence are improving rapidly and getting mature at a very fast pace, it is a challenge to stay on top of the changing land scape of tools and technologies in big data technology stack. At big data dimension, we believe in re-using the existing skills, infrastructure and assists to shift smartly to big data platform technologies. Our recommendation is as follows

  • We advise that customer should try to use the market available tools to reduce the need to learn coding scripts/syntax and instead of take advantage of Visual tools like Talend which can enable your existing ETL developers/DWH Consultants to easily shift and re-write the data management logic in Talend which on the back end automatically translate it to the native Hadoop (Map reduce ,Java Code).

There are couple of more visual tools available in the market to help you get speed up and easily adapt to Big data technologies.

Speak to our experts today in order take some help on Big Data, Talend, AWS, MDM, Azure, BI implementation or training about its features and how effectively these technologies can be used to upgrade your staff skills and meet the current challenges without losing existing staff and resources.

Big Data technologies are not only the solution to Data analytics or predictive analytics it can also play a very vital role in improving the performance and efficiency of OLTP ( Transactional systems , web portals , mobile applications).  Our data engineers can analyses your existing applications to suggest the changes that can results in better customer service and customer experience.  Some of the ideas are as follows

  • By using a No-SQL data base (e.g. H base , Casandra , Amazon AWS DynamoDB) and integrate with the web portal , all the sessions information stored on a NoSQL database and hence providing better customer experience , work load management and the giving a scalable design to handle massive traffic on the web portals.
  • By ingesting the near real time Click stream data using technologies like Apache Kafka ,Storm or AWS Kenisis and provide recommendations to improve customer experience and potential

For Further details get in touch with our Sales team. Our team will be happy to make an assessment and recommend customized solution per your needs.

Are you worried about growing data warehouse cost on expensive MPP databases?

Get the best scalable and real-time analytics approach for offloading heavy structured and unstructured data loads & associated data from the data warehouse to Hadoop

Shifting expensive workloads from enterprise data warehouses into Hadoop & Spark can be intimidating. How do you know where to begin, and what will deliver the most savings?

Start your Bigdata Lake journey on Hadoop, Spark, AWS, Azure and NoSql with our proven Bigdata Lake Customer 360 solution to save money while building your enterprise Bigdata Lake hub to power next generation big data analytics.

Enabling Enterprises To Be Big Data Lake Driven

Benefits of Bigdata Lake Customer 360 Solution

  • Blend all types of data and transform into meaningful insights.
  • Get recent real-time data on right time.
  • Enhance data warehouse performance by pushing loads into Hadoop data lake.
  • Save MPP database storage and cost – estimated saving from $200K/TB to $1000K/TB and more.
  • Blend new structure and unstructured data fast and secure in Bigdata Lake.
  • Bigdata Lake provides virtually limitless capacity to scale.
  • Data can be collected and encrypted easily.
  • Highly secure and compliant architecture.
  • Offers excellent support for real time analytics.

Turn to Bigdata Dimension for building scalable BigData Lake solution with our proven Bigdata Lake Customer 360 solution. By aligning your business needs with our solutions, we can support both short and long-term objectives within highly desirable cost schedules.

Let Us Help You – With Your Bigdata Lake Needs

Enabling the Enterprise Bigdata Lake Hub

Remove barriers to build Bigdata Lake on Hadoop, Spark, Hive, AWS, Azure & more

Our Bigdata Lake Customer 360 solution removes barriers to mainstream Bigdata Lake adoption & deliver scalable data pipeline architecture for collecting, ingesting, blending, transforming, publishing and distributing data with Hadoop, Spark, Hive, AWS, Azure and more relevant technologies.

We make it easy to make BigData Lake into ideal staging, blending, prepping and transforming area for all your structured and unstructured data on high performance massive parallel system where you can execute all the batch workloads and then feed it to other downstream systems from a centralized location.

We make it easy to offload your heavy duty data warehouse loads on Bigdata Lake platform and build scalable operational data store and Data Warehouse soltution on Bigdata Lake Cluster. Effectively offloading workloads from data warehouse into Bigdata Lake lets you slash batch windows, keep data freshly available based on your need, and frees up significant data warehouse storage

Data lakes follow the Data and Analytics as a Service (DaAaaS) model that resembles a Hub and Spoke. The data forms the centre of this multitenant architecture. This data is captured from different sources like social media, call logs, webserver logs, databases etc., and is stored in the data catalog.
Our Data Lake solution integrates unstructured data and Hadoop with existing traditional enterprise/legacy data and file system. The result is world-class data analysis – amplified by the use of Big Data technologies.
Optimum data analysis becomes a reality when the technology of Hadoop is being enmeshed in regular business efforts and hence data analysis simply becomes a cakewalk. To be precise, when data is programmed to be distributed across the multiple platforms, its ultimate analysis has been made possible while diligent Hadoop technology is being put in use.

A range of useful applications can be installed which are compatible with purposeful Hadoop technology and hence businesses can secure excellent customer experience, alongside enhanced quality of service which would pave way for greater purchasing capability than earlier or than the rest.

hadoop-need-big-data-lake

Our Preferred Recommendation on Building Bigdata Lake Strategy:

Sans doubt but seeking an expert advice always plays a pivotal role in managing business technology and terms with outside world and hence useful recommendation for building Bigdata Lake strategy have been the key to lucrative factor to businesses in a range of fields. For instance, from banks to online retailers to insurance providers to healthcare to entertainment and so forth, Bigdata Lake strategy can pull up a thunder in regard to positive business experience and on the strength of positive Big Data technology.

As such, an integrated Big Data framework is the most exceptional, flexible, scalable and the most pocket friendly technology investment for expansive businesses to re-direct their phenomenal approach in business marketing efforts., over the past a few years. For seeking more enlightenment about Big Data technology, count upon our acclaimed Big Data expertise in the field and we deliver scalable and customized solutions to businesses as per their requirement and budget.

Our Customer Centric Technology Is Key To Optimum Business Success:

A diversified technology service resulting from Big Data Diligence, has been the greatest skill virtue for BigData Dimension and our programmers accomplish engine recommendation, analysis customer preferences and likes and in drawing explicit entity identification, which, taken together constitute invigorative business information to tap upon. Hence, our programmers invoke the know-how of latest pool of technologies such as Hive, Pig, Mahout and a range of other Hadoop based technologies which are master driven by our digital stalwarts, innovatively.

Gain the expertise of BigData Dimension experts with your Bigdata, Hadoop & Data Lake implementation needs. Speak to our experts today to discuss more information about our Bigdata, Hadoop and Data Lake offerings.

BigData Dimension build Bigdata Lake  with competitive consulting Involving latest set of technologies such as Apache Cassandra and DataStax to those enterprises who simply adhere to forward looking approach to get aligned with evolving technology. Clearly, Apache Cassandra is an open source technology which also happens to be diligently distributed database management framework which intends to address the hurdles that businesses face in the light of heavy data deluge. Besides, there is also present an in-built capability to merge and manage widespread data which is scattered across product servers  and which provide relentless efficiency with minimal fault possibility.

Hence, BigData Dimension is fully capable to make optimized use of available third party tools, tested methodologies and to harp upon the delectable and modern database skills to enable organizations to cruise elegantly on the global platform on the underlying power of Cassandra and DataStax technologies to build Big Data Lake. There is an undeclared promise that corporations would witness renewed business spirits with  integrated aspects of reliability, security, seamlessness and we deliver the ever accessible databases which are all targeted to swiftly execute Cassandra based projects.

cassandra-need-bigdata-lakeOur widely experienced Cassandra pundits are ever ready to assess business cases, and to carve out the proofs of concepts (POCs) which is followed by the designing phase and architecture blueprint is developed thereby, which is based upon such a relishing and latest set of technology.

As such, our service basket includes every single aspect in regard to typical Cassandra Bigdata Lake projects, such as from architecture blueprint to implementation followed by migration to this platform to its underlying optimization which is always be according to established standards of Cassandra database management service.   Likewise, we remain devoted to extend seamless support 24X7 and remain available 365 days all round year and our qualified DBAs and database scientists can integrate the database projects with other solutions such as Apache Hadoop, Apache Spark and Apache Kafka.

Finally, on ranging platforms such as cloud based to on premises, Datastax Cassandra has been the specific expertise that BigData Dimension is always devoted to such crucial business needs, while keeping competition in sight.

A super versatile open source technology which savours distributed processing and is the most primary choice among those grappling with big data deluge in their organizations who intend to simply orderly order such a crucial progress fuel and who also want to address the associated challenges of building high performance Bigdata Lake. Additionally, it has been such a uniquely integrated framework which is primarily designed with a cache that is purposefully being placed inside the memory and then the processing power is also refurbished massively than the other platforms of the series in the present era which makes Spark as the genuine candidate for building scalable Bigdata Lake.

All such factors have been done so as to seek optimization and an enhanced throughput and to address efficiency and productivity element among the workforce. Besides, the ever preferred features of batch processing, machine learning, graphical databases, streaming analytics and random queries facility is also being programmed into the framework with delicate tech precision, when the Apache Spark is at play.

Further, for the exclusive purpose of Hadoop YARN, there is special emphasis for it that is implicitly crafted, under the Amazon EMR in wake of which, the users are enabled to diligently cum easily craft Apache Spark clusters in the most managed and measured manner and such a facility is duly designed into AWS Management Console, AWS CLI or at Amazon EMR API.  In an even grander scheme of things, subsequent Amazon EMR characteristics can easily being leveraged substantially, such as Amazon S3 connectivity with special reference to Amazon EMR file system (EMRFS), groundbreaking alignment with Amazon EC2 Spot Market and then certain resize commands also relish the programmers and far-sighted developers such as to add and to delete a portion within the specific cluster. Finally, there is provided Apache Zeppelin which facilitate swift crafting of interactive and collaborative notebooks with which data can easily explored while one unravels layers of diligence over Apache Spark framework.

Sans doubt, but since its emergence, the technology is widely hailed and is finely tuned with, by the businesses from across the globally spanning industry and owing to its surplus prominence, even the internet giants such as Yahoo, eBay and Netflix have shown strong favour for Spark, in order to spark their functional performance and business automation, on a wider scale. Pleasant consequence, enchanting scenario is seen whereby vast petabytes of data gets processed at a wider magnitude and over a distributed segments of over 8000 nodes. As the time elapsed, the technology has gained immense momentum as the most advanced open source technology in terms of big data and then, it is heavily being contributed to, by passionate programmers and inventive coders from a vast section of industry corporations, which simply numbers to over 250.

spark-need-bigdata-lake

Some of the prominent advantages of building Bigdata Lake on Apache Spark are as follows:

Enhanced Speed & High Performance:

The most striking feature of such a design is that it has been designed with a bottom up approach and each of the layer is painstakingly designed to support the upper and lower one and the experience shows that Apache has proved to be much faster than Hadoop where the software has to manage vast deluge of data to process. Further and as mentioned above, memory computing has been introduced in Apache Spark where cache is being placed under main memory itself for faster retrieval of data and mathematical signs and the eventual expression. Finally, data is designed to be resident of disk and then it is marked with a wide scale on-disk sorting for enhanced processing.

Extremely User Friendly:

Spark is implicitly designed with user friendly APIs which is meant for swift handling of large databases and then, there is a unique assortment of over 100 operators which simply undertakes intrinsic conversion of data and other chunks of data frame APIs in a bit to manipulate unstructured or mess up data.

Integrated Engine With A Secured Framework:

Spark is pre-packed with enormous libraries of high level composition and it also includes seamless support for SQL queries, and also gives way to sequential data streams. Not only this, the machine learning phenomena makes Apache Spark to be all round favourite and the elegant graph processing also highlights the deep rooted sophistication of such an astute processing framework.

BigData Dimension: Bearing Flag of Sparkling Performance By Taming Spark To Industrial Functioning by Building Bigdata Lake

As is well known, that Apache Spark is an open source technology and is not controlled or is managed by any specific vendor and tech group and hence with sheer dedication, our tech stalwarts are one of the widely sought after minds by the business organizations who favour this technology at length and who want its alignment as the major process manipulation framework of their corporate and business affairs for building high performance Bigdata Lake.

Presently, the Apache Spark is proudly being hosted at Apache Software Foundation and alongside the Apache Spark zealots, BigData Dimension digital pilots, also favour the gross software foundation refinement. In a nutshell, transforming the Apache into friendly implementation is what the paramount skill of our prime time coders and to keep the torch of enlightenment and innovation high and well lit, is the ultimate objective of BigData Dimension.

Bigdata Dimension recommends building and processing Bigdata Lake on Apache Storm & Apache Ignite as Storm and Ignite reliably process unbounded stream of real-time data and has the capacity to process over a million tuples per second per node and guarantees data processing.

In wake of staggering globalization and which is marked by fierce competition among businesses, drawing useful insights and quickly actionable information from a large pool of records and hefty databases, has emerged as the crucial need of today. Likewise, even the data records have gained volume as businesses are always found to be in expansive mode owing to fast evolving digital technology. For instance, new set of engines which are broadly classified as Complex Events Processing (CEP) but which have a common term of identification as Distributed Computation Engines. Certainly, such engines are intended to replace ETL since such are widely used for similar set of functions which ETL was relied upon for earlier.

storm-need-bigdata-lake

Notwithstanding, CEP are intrinsically designed to alter the processing schedule as per message influx, rather than adhering to batch processing schedules. Hence, while reckoning about the most notable platforms, one can easily uncover Storm, ESPER and Akka under such a distinct class of exceptional frameworks. Moreover, all three of these happen to be from open source clan of progressive technologies and are immensely scalable, integrated and have won hearts from thousands of businesses worldwide owing to their brilliance in performance.

As a relishing feature of a sophisticated application architecture, target system landing is bypassed and hence batch processing can be scheduled for later period. As such, the analytics system emerges as the most striking aspect of such a framework which is concerned with strategic application architecture, enabled in an integrated business environment.

Unlike Hadoop, Storm keeps the topology running forever until you kill it. A simple topology starts with spouts, emit stream from the sources to bolt for processing data. Apache Storm main job is to run the topology and will run any number of topology at given time.

Storm: Stepping Stone To Big Data Solution Part:

Commonly said, “Necessity is the mother of invention” and hence, newer set of technology has evolved to meet the operational challenges that have emerged traditionally earlier. Precisely, when the strength of users over Twitter and the total number of Tweets soared, necessity was felt for having a processing scale for efficient handling of such posts. As a creative reaction, Strom was crafted to enable an indepth analysis and to guide the increasing number of messages to separate category. As such, when we have a tool to handle the process of millions of messages per second while ensuring full message guarantee, we come up with an amazing engine paving way for having a real time analytics. Gradually, such a tremendous technology, has been ingrained by scores of industries worldwide from various fields, while all of them have registered exciting success and versatile agility.

Dominant Expertise:

In such a concept, BigData Dimension exercise exceptional deftness and our seasoned digital stalwarts implement high volume / low latency analytic solutions in regard to such accomplished platforms. Contact us today and relish a demo of our digital prowess while we  get prepare to cater your business centric issues in regard to CEP/ Storm.

Meticulous Consulting For Big Data Technique And NoSQL Databases

During past decades, skilfully integrated relational databases have been our prime tools to revert to and such are still performing excellently in present times while there have been developed tons of premium tools and utility programs which are simply embedded with such databases. Hence, NoSQL has emerged as the most seasoned technology which takes great care of different datasets and data transmission protocols and act like a resourceful solution to ever ending business issues for building Bigdata Lake.

Bigdata Dimension recommends building Bigdata Lake on NoSql Databases like MongoDB, AWS DynamoDB, Hbase etc. Turn to Bigdata Dimension for building scalable BigData Lake solution on NoSql databases. By aligning your business needs with our solutions, we can support both short and long-term objectives within highly desirable cost schedules.

NoSQL : Technology Hailed With Great Agility: 

There have surfaced a range of tough challenges before the most sophisticated global audience of today and businesses have been at the receiving end of such amorphous operational and security problems. But there are made available customized databases which are replete with modern capabilities and are more powerful than their counterparts of earlier times. In addition to this, modern means of database solutions are completely flexible and can easily be manoeuvred through key commodity and inventory servers and can house petabytes of data, insulated by utmost sophistication.

As a greater invoking to creative nerves, host of creative modelling and plush designs have been introduced, such as visualised graphs, explanatory diagrams, notifying key values, legible document designs and so forth, while such savouring delights can secure enhanced flexibility while data storage aspect is concerned as in the case of relational format.

Analytic Stores: House of High Decibel Digital Performance :

The most striking characteristic of NoSQL database has indeed been the ingrained virtue of otherwise huge volume of data but at very low latency and as data volume keeps swelling, the analytic agents are delivered with better insights from such expansive data but at lower latency. Sans doubt, aligning around a million messages in a jiff would seem to be a remote dream but, by the virtue of high quality performance columnar or in key value store, such a requirement could be met with a relatively low hardware implementation.

NOSQL: Technology Cruising Across Global Shores With Its Abounding Impact:  

Overwhelmed with NoSQL’s widespread approach, it clearly seems that such databases are being crafted almost on daily basis. All such databases are found to possess slightly different technology and functional capabilities. Hence, selecting the appropriate product and with explicit practices in place, this has been the everyone’s favourite flavour when it comes to maintain comprehensive and widely distributed database. However, the downside of such framework is slower rate of support from its community members but with BigData Dimension’s towering expertise and proven performance, we are right place to knock at, while looking to automate your business operations.

We are simply the bunch of digital architects and we have literally enabled scores of corporations from various fields in drawing the dividends from such a tech driven database framework.  Simply contact us to know more and to explore more possible ways to get benefited.

Millions Emails per min
Million Facebook likes Per Min
Tweets Per Min
Search Queries Per Sec
Photos uploads per min
Video hours per minute

Our Approach

Ideate

We can absorb an idea and create an strategic roadmap for BigData Lake. This steps includes

Research

An idea can never come to fruition without research. Be it sifting through the customer requirement and understand the current landscape.

Brainstorming

We think sift through various ideas to create the next generation high performance data lake that work best. It helps to materialize the thought process and help in building all-muscled powered solution.

Prototype

We prototype the idea to bring to life the thoughts, and research.

Advise

We identify and optimize the underlying points where enriching Big Data initiatives can be implemented.

Bigdata Lake Advisory

An idea can never come to fruition without research. Be it sifting through the customer requirement and understand the current landscape.

Tool Evaluation

We think sift through various ideas to create the next generation high performance data lake that work best. It helps to materialize the thought process and help in building all-muscled powered solution.

Design

We convert abstract ideas into meaningful Bigdata Lake design by prepping, Ingesting and building metadata repository.
This section includes

Data Prepping

An idea can never come to fruition without research. Be it sifting through the customer requirement and understand the current landscape.

Data Ingestion

We think sift through various ideas to create the next generation high performance data lake that work best. It helps to materialize the thought process and help in building all-muscled powered solution.

Metadata Repository

Build metadata repository to maintain the schema, data profiling and data cleaning process.

Data Traceability

Build data lineage and batch processing to track data lineage.

Change Data Capture (CDC)

Build CDC mechanism to fetch the delta load.

Transform

We transform structured and unstructured data sets in Bigdata Lake. This section includes

Prepare Data

Build standardized structure of common and custom data sets.

Data Profile

Perform data profile process to check anonymity on data.

Data Quality

Perform data cleaning rules to make golden copy of the record.

Data Governance

Perform integrity and security process on data.

Data Masking

Apply data masking and encryption on sensitive data.

Analyze

Perform data analysis to transform the data into information

Build

We build custom solutions on Bigdata, MDM, Talend, Cloud, Data Warehouse, BI and application development. This section includes

Publish Data

Provide access to data semantic layer to analyze what has occurred.

Data Consumption

User can test the data from the semantic layer.

Data Visualization

User can visualize the data from the semantic layer.

Manage

We ensure proper training, support and administration service is provided. This section includes

Training Solution

Provide customer training

Support Solution

Provide Change and growth management solution

Administration

Provide administration and configuration solution.

Our Services

Configuration & Installation Services

We provide Hadoop Configuration & Installation services.

Architecture & Development Services

We provide Hadoop Data Lake Architecture and Development Services.

Upgrade Services

We provide speciality services to upgrade your existing Hadoop Bigdata Lake

Support Services

We provide managed and production support services

Training Services

We provide training services to our customer to bring them upto the speed on the delivered work.

Our Deliverables

This type of engagement typically produces:

  • Confirmation of business requirements
  • Bigdata Lake Advisory Solution
  • Tool Evaluation Solution
  • Bigdata Data Lake solution design and implementation
  • Comprehensive Bigdata Lake system documentation
  • Configuration and Administration Solution
  • Support personnel and user training
  • Change and growth management support
  • Training Solution

Our Process

We follow agile methodology and our project run in Sprints: iterative cycles of requirements gathering, analysis, design and development – that are focused on a given subject area. We utilize JiRA and other ticketing tools to manage project releases in an agile way.
We follow lean principles of Scrum to develop project iteratively and present the solution with the use quickly. This helps in getting feedback sooner and any issues reported by the user will be fix quickly.

Our Implementation Team

To plan, build, implement and support Real-Estate Analytics, BDD uses an industry experts and further provide our own proven development methodology, and will function closely with your implementation need. Project teams typically include the following roles and responsibilities:

  • Project Manager
  • Business Analyst
  • Bigdata Architect
  • MDM Developer
  • DBA
  • Java Architect
  • Cloud Architect
  • Bigdata Developer
  • ETL/ESB Architect
  • ETL Developers
  • Java Developer
  • .Net Developer
  • MDM/Java Architect
  • Data Architect
  • Java/MDM Developer
  • MDM Testers
  • .Net Architect

SUCCESS STORIES BY DOMAIN

Managed & Advisory
Services

ENTERPRISE GRADE PROFESSIONAL SERVICES & CUSTOMISED SOLUTIONS

Bigdata lake Beam 360 Starter
Solution

ENTERPRISE GRADE PROFESSIONAL SERVICES & CUSTOMISED SOLUTIONS

MDM Beam 360 Starter
Solution

ENTERPRISE GRADE PROFESSIONAL SERVICES & CUSTOMISED SOLUTIONS