Big data, an evolving term in the IT industry represents the information assets characterized by 3V’s (Volume, Variety and Velocity) 
Big data leads to big challenges, and these challenges are because of the 3 important properties of Big data (Volume, Variety and Velocity).
Volume refers to the available data that are out there and need to be assessed for relevance, and businesses usually capture and generate vast amounts of data.
Velocity refers to the speed with which data gets generated and processed, and as big as millions of events need to be processed per second.
Variety in Big Data refers to all the structured and unstructured data that humans or machines capture or generate. Data like text, images and videos are considered as structured, while emails, hand-written text, audio recordings etc, are considered as unstructured data.

We help businesses convert big data challenges into business opportunities

Businesses have immense volume of data that cannot be processed using traditional data processing softwares. Big data analytics is the process of examining Big data and transform it into actionable information.
Enlume has helped many organizations boost their effectiveness by implementing data science to solve complex business challenges. Our team of architects, engineers and data scientists work with enterprises to build a roadmap to success with Business Intelligence and Big Data Analytics.

Big Data Services

What can Big Data with AWS and EnLume do for your business?

Optimize query performance and reduce costs by deploying your data warehousing architecture on AWS.
Securely store all of your data in one place and make it available to a broad set of processing and analytical engines.
Improve your customers digital experience and gain a better understanding of your website.
Collect, process, and analyze data in real-time.
Add predictive capabilities to your applications.
Use AWS Lambda to perform data transformations – filter, sort, join, aggregate, and more – on new data.

Why AWS & EnLume for Big Data

Unlike on-premises Big Data practices, which require significant upfront investment, AWS allows you to provision what you need to support your workloads and pay-as-you-go. There’s no lead time required for provisioning, you can scale up and down as needed, and you’re never locked into a contract or stuck paying for hardware you don’t need.
A broad and deep AWS platform means you can build virtually any Big Data application and support any workload regardless of volume, velocity, and variety of data. With 50+ services and hundreds of features added yearly, AWS provides you everything you need to collect, store, process, analyze, and visualize Big Data in the cloud.
Most Big Data technologies require large clusters of servers resulting in long provisioning and setup cycles. With AWS you can deploy and scale the infrastructure you need almost instantly with no upfront costs. This means your teams can be more productive, try new things, and roll out projects sooner.
AWS provides capabilities across facilities, network, software, and business processes so that you can deploy the strictest
of security requirements. Environments are continuously audited for certifications and assurance programs help customers prove compliance for 20+ standards on the policies, processes, and controls that AWS establishes and operates.

Shifting Data Lakes to the Cloud

Data lakes originated to help organizations capture, store and process any type of data regardless of shape or size. With the proliferation of Cloud Services, enterprise data lakes are moving to the cloud because the cloud provides performance, scalability, reliability, availability, a diverse set of analytic engines, and massive economies of scale.
AWS is a clear choice for implementing a data lake, offering the necessary data services to ingest, store, process, and deliver insight out of the data lake. However, one promise of a data lake has been to democratize data access and intelligence by enabling a larger number of analytics users to work with a broader diversity of data in a self-service fashion. This is where data preparation comes into the picture as a critical component of an AWS data lake environment.
EnLume, an AWS advanced tier consulting partner with over a decade of experience architecting, engineering, scaling and managing modern enterprise systems in the cloud. We enable data-driven decision making through our extensive experience in data management, data warehouse implementation, real-time data integration, high volume data processing, data orchestration and reporting.

Why You Should Build a Data Lake on AWS

AWS provides a highly scalable, flexible, secure, and cost-effective platform for your organization to build a Data Lake – a data repository for both structured and unstructured data. With a Data Lake on AWS, your organization no longer needs to worry about structuring or transforming data before storing it. You can analyze data on-demand without knowing what questions you’re going to ask upfront.

Capabilities of an AWS and EnLume Data Lake

Cost effectively collect and store any type of data at scale.
Protect data stored at rest and in-transit.
Easily find relevant data for analysis.
Quickly and easily perform new types of data analysis.
Process on-demand
Only process data as needed at time of use.


Data Preparation in the AWS Data Lake

While many organizations are making investments in data lakes, they are struggling to scale these platforms to broad adoption because the longest part of the process – getting data ready for business consumption – is consistently too cumbersome. In order to alleviate this problem, EnLume partnered with Trifacta, a data preparation tool that leverages typical AWS data lake services such as Amazon S3, Amazon EMR, or Amazon Redshift to enable data scientists, data engineers, and other data and business analysts to benefit from the abundance of data typically landed in Amazon S3. Trifacta allows the users with the greatest context for the data to more quickly and easily transform data from its raw format into a refined state for analytics and/or machine learning initiatives.
The primary role of Trifacta is to enable data lake users to wrangle data in a particular zone and in the process move it from one zone to another zone to fulfil a particular data process. Trifacta seamlessly integrates with AWS by reading and writing to Amazon S3 (often raw or intermediary data lake zones) and to Amazon Redshift (for the more refined data zone). Our platform also leverages Amazon EMR to execute preparation recipes at scale and output data to the next stage in the refinement process of the data lake.

Simplified AWS Data Lake Architecture

Architecture of an AWS based data lake with Trifacta Wrangler Enterprise


Helped a healthcare cost management client increase revenue by 1.5X from key accounts:
We have built a claim process management framework that can process millions of claims in real-time and provides KPI’s. The system has helped the client increase their revenue by 1.5x from key accounts.
Helped solve $1.2bn inventory problem of liquor industry using Big data and IoT:
We helped our client in implementing their innovative idea of providing real-time inventory information across distribution channels using custom built sensors, Big data and IoT. This saves the sellers from stock out situation and prevents sales loss.

Technology Stack we use


AWS-DynamoDBAWS DynamoDB
Postgre SQLPostgre SQL
AWS-GlacierMy SQL
Graph DBGraphDB


aws-ec2AWS EC2
aws-lambadaAWS Lambda
aws-emrAWS EMR


aws-sesAWS SNS
aws-snsAWS SES


aws-cloudAWS CloudTrail
cloud-watchAWS CloudWatch


CodePipelineAWS CodePipeline
CodeCommitAWS CodeCommit
CodeBuildAWS CodeBuild
CodeDeployAWS CodeDeploy

Networking & Content Delivery

vpcAWS CloudFront
cloud-frontAWS VPC


sparkAWS SageMaker
emrAWS Kinesis
kinsesAWS EMR

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