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What Is a Case Study?

When you’re performing research as part of your job or for a school assignment, you’ll probably come across case studies that help you to learn more about the topic at hand. But what is a case study and why are they helpful? Read on to learn all about case studies.

Deep Dive into a Topic

At face value, a case study is a deep dive into a topic. Case studies can be found in many fields, particularly across the social sciences and medicine. When you conduct a case study, you create a body of research based on an inquiry and related data from analysis of a group, individual or controlled research environment.

As a researcher, you can benefit from the analysis of case studies similar to inquiries you’re currently studying. Researchers often rely on case studies to answer questions that basic information and standard diagnostics cannot address.

Study a Pattern

One of the main objectives of a case study is to find a pattern that answers whatever the initial inquiry seeks to find. This might be a question about why college students are prone to certain eating habits or what mental health problems afflict house fire survivors. The researcher then collects data, either through observation or data research, and starts connecting the dots to find underlying behaviors or impacts of the sample group’s behavior.

Gather Evidence

During the study period, the researcher gathers evidence to back the observed patterns and future claims that’ll be derived from the data. Since case studies are usually presented in the professional environment, it’s not enough to simply have a theory and observational notes to back up a claim. Instead, the researcher must provide evidence to support the body of study and the resulting conclusions.

Present Findings

As the study progresses, the researcher develops a solid case to present to peers or a governing body. Case study presentation is important because it legitimizes the body of research and opens the findings to a broader analysis that may end up drawing a conclusion that’s more true to the data than what one or two researchers might establish. The presentation might be formal or casual, depending on the case study itself.

Draw Conclusions

Once the body of research is established, it’s time to draw conclusions from the case study. As with all social sciences studies, conclusions from one researcher shouldn’t necessarily be taken as gospel, but they’re helpful for advancing the body of knowledge in a given field. For that purpose, they’re an invaluable way of gathering new material and presenting ideas that others in the field can learn from and expand upon.


data lake case study aws

Amazon Migrates 50 PB of Analytics Data from Oracle to AWS

Ending Support for Internet Explorer

Implementing a Large AWS Data Lake for Analysis of Heterogeneous Data

customer logo

C4ADS users were finding it increasingly difficult to sift through the company’s massive database collection.

ClearScale implemented a data lake with an Amazon Virtual Private Cloud (VPC), designed a web-based user interface, and used AWS Lambda and API Gateway to ingest data.

C4ADS’ new solution can scale as needed without compromising security and is able to meet user demands more effectively.

AWS Services

Amazon Virtual Private Cloud (VPC), AWS S3, AWS Lambda, Amazon API Gateway, Amazon CloudWatch, AWS CloudTrail, Amazon DynamoDB

Executive Summary

C4ADS (Center for Advanced Defense Studies) is a nonprofit organization based in Washington DC that is dedicated to providing data-driven analysis and evidence-based reporting on global conflict and transnational security issues. In this pursuit, C4ADS focuses on a variety of issues, including threat finance, transnational organized crime, and proliferation networks.

The Challenge

The world is a complex ecosystem of people, economies, competing interests, and political ambiguity. Being able to track many different events to determine if there are patterns that would warrant a more critical look and analysis is a difficult task, even under the best conditions. With new regional or political developments each day, sometimes even hour by hour, combing through enormous sets of data is challenging; especially when that data is from different sources and in various formats.

C4ADS is tasked with just this sort of activity. Their clients require evidence-based and data-driven analysis concerning global conflict and transnational security issues. With a focus on identifying the drivers and enablers of such conflict, this organization has to be absolutely confident in the analysis and assessments they provide. However, the first step to performing any sort of review requires analysts to comb through extensive records from different sources and formats to compile a list of potential hits.

As C4ADS increased the number of datasets it ingested, new challenges arose, specifically the ability to make use of all the data at its disposal. As more and more data has become available, their analysts were finding it difficult to sift through all of the incoming information in a quick and expedient way. The company approached ClearScale, an AWS Premier Consulting Partner, and wanted to see if there was a way that they could leverage what they did currently by using AWS to assist in making the data more user-friendly.

The ClearScale Solution

The challenge put forth by C4ADS was that a solution had to be implemented quickly, provide the ability to scale as needed, and be extremely secure given the nature of the information they were reviewing. With these three criteria in mind, ClearScale reviewed various designs and approaches that they could develop and implement on AWS.

Data Storage with Data Lake Approach

The biggest challenge was finding a way to aggregate multiple different file formats (such as PDFs, emails, Microsoft Word and Excel files, logs, XML and JSON files) while still allowing C4ADS to perform easy searches within a large data repository. It rapidly became clear that to accomplish the requirements laid out by the client, ClearScale would have to implement a Data Lake approach within an AWS Virtual Private Cloud (VPC). Unlike traditional data warehouse methodologies that require data to conform to a specific set of schema, a data lake allows for any number of data types to be stored and referenced, so long as those data types have a consistent approach to querying and retrieving data.

It was immediately clear that trying to collapse or conform all the various file types that were available into a normalized format would be too resource-intensive. To overcome this, ClearScale chose instead to implement a solution that would tag all uploaded file content with consistent metadata tagging which, in turn, would allow for greater visibility and speedier search results. This automated metadata tagging for each file that was uploaded either manually or via bulk upload would mimic the client’s existing folder structure and schema that they had adopted internally. This approach would ensure that the new solution would be easily understood by analysts that were already familiar with the current operational processes.

Data Flow Model

Data Flow Model

System Architecture Diagram

System Architecture Diagram

Web-Based User Interface (Web UI)

To access and search these records, ClearScale designed and implemented a web-based user interface. This UI was designed to allow for complete management of the data sources — including data upload — beyond simply searching the Data Lake. From a data repository perspective, ClearScale needed to build and deploy a solution that was scalable and reactive to increased demand but also highly secure. To accomplish this, a combination of AWS S3 was used for the storage of the data uploaded, and DynamoDB for the storage of the file metadata; ElasticSearch was used for the robust search querying that was required.

In order to get the data uploaded, ClearScale leveraged AWS Lambda and API Gateway services to properly ingest the data and automate the creation of the file metadata. Both CloudWatch and CloudTrail were also put in place to monitor resource usage and serve as triggering mechanisms to scale the environment as required.

The entire solution was encased in AWS VPC for robust security and Cognito for SAML based authentication. This approach guarantees that the information was behind a robust security layer with additional work done for data to be encrypted both at rest and in transit. It also insured that administrators could grant access to specific document types based on group roles, both for internal and external role types.

UI Welcome Screen

UI Welcome Screen

Bulk Indexing — Add and Index an existed S3 Bucket or Folder

Add and Index an Existed S3 Bucket

Bulk Indexing — Monitoring of Long Time Backend Tasks

Monitoring of Long Time Backend Tasks

Bulk Indexing — Login and Automatic Errors Handling

Login and Automatic Errors Handling

Multi-tenancy — Agile Access Setup

Agile Access Setup

Metadata — Governance


Cart — Storing and Exploring Results in Personal Cart

Storing and Exploring Results

The Benefits

The turnaround time from design to delivery to C4ADS was a mere two months, including deployment of the solution in both a Staging and Production environment as well as training for C4ADS staff on how to use the new solution. The first release provided everything that C4ADS originally asked for: it had to be deployed quickly, it had to have the ability to scale as needed, and it had to be highly secure. Launched in October 2017, the solution has already optimized the analysts’ job activities by giving them the tools necessary to do wide-ranging search profiles and aggregate disparate heterogeneous data types.

Later releases will introduce more robust security measures that will allow C4ADS to extend the service out to their partner organizations. It will also provide multi-lingual support and optical character recognition (OCR) technology to aid in identification of important data markers in the data that is uploaded.

There are plenty of challenges in the business and technology landscape. Finding ways to overcome these challenges is what ClearScale does best. By bringing our own development resources to bear on these complex problems, we can design, build, test, and implement a solution in partnership with your organization, thus allowing you to focus on more pressing matters in running your day-to-day operations.


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1CloudHub: Digital Transformation – Advisory | Solutions | Implementation

Case Study: Enterprise data lake on cloud

1CloudHub helped India’s leading television entertainment network bring its scattered big data into a single source of truth, to make advanced analytics affordable.

Cloud Advisory Services

Dc, servers & data, project scope, — data lake architecture design — data transformation and storage in data lake — customized reports in powerbi, about the client.

The client is a leading media production and broadcasting company, subsidiary of a global media conglomerate. They have over 30 television channels, a digital business and a movie production business, reaching over 700 million viewers in India.

Business challenge

As part of their digital strategy, our client wanted to optimise user experience across channels — iOS and Android apps, Fire TV, web, and so on — based on user behaviour and preferences. This required a deeper understanding of customer behavioural patterns across platforms.

Presently, they were using Segment as the tool to collect around 6.5 billion records (20TB of raw data) of behavioural data from their 30 million online viewers every month from across sources.

In order to deliver a user-focussed digital viewing experience, the client needed

We, at 1CloudHub, enabled an enterprise data lake for all of the client’s data to reside in one place — preserving accuracy and timeliness of the data.

Leveraging our client’s existing mechanism to collect and feed data into the data lake, we created a pipeline with EMR (Elastic MapReduce) for data crunching or ETL (Extract, Transform, Load) and Power BI for self-service visualisation.

Our approach

data lake case study aws

Completion and reporting

01. understand.

data lake case study aws

04. Transform

05. Completion and reporting


We enabled advanced analytics for data from up to a year — compared to the 3 months data as per agreement — to deliver the meaningful insights the business teams sought.


We crunched over 12 million records in under an hour, running more than 100 VMs concurrently in a cluster.


We delivered each report at a cost of $70. At this cost, we delivered an excellent price-to-performance ratio, driven by the spot fleet instances we used and our on-demand or pay-as-you-use cloud model.

A similar setup on-premise in a data centre would have cost the client 12,000 times more.

Looking forward

We are delighted to have helped the client create a centralized, analytics-ready repository for their Big Data and look forward to helping them meet their strategic goals using our cloud capabilities.

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AWS Data Lake - Azure Data Lake - ML/AI Case Studies

Case Studies

Aws data lake project, azure data lake project, ml/ai projects.

Data Engineering

Data Engineering

Industry: Consulting

Subject: AWS Cloud Data Lake Development; Cloud Big Data Engineering


Developing and maintaining data lakes on AWS. Data migration from RDBMS and file sources, loading data into S3, Redshift, and RDS. Designing and developing big data batch solutions using AWS Data Pipeline and AWS Glue and EMR. Developing a massive data warehouse using Redshift and Redshift Spectrum.

Project Task Summary

ETL workflows in Data Pipeline, monitoring and management of ETL pipelines .

Batch RDBMS data migration using AWS DMS .

Batch processing in EMR and Glue using Scala Spark.

Designing and developing data warehouse on Redshift.

DWH data model and table design .

Accessing and processing big data on S3 via SQL using Redshift Spectrum.

Python ML implementation with Pandas, scikit-learn using Jupyter on AWS.

CI/CD development using Gitlab and Ansible.



Subject: Cloud Data Lake DevOps; AWS DevOps

Provisioning and deployment of big data solutions on AWS. Operationalize cloud data solutions, implementing infrastructure as code (IaC), using CloudFormation templates for resource management. Provisioning and deploying on-demand Redshift cluster and RDS instances using CloudFormation. Development, management, and deployment of Docker images and containers.

Provisioning resources using CloudFormation templates .

Provisioning of Redshift, Data Pipeline, and Glue ETL pipelines .

User account and access management in IAM.

Develop Docker images for batch processing applications and Python, ML models, using AWS Container Registry (AWS ECR) .

Docker container deployment using AWS ECS .

CI/CD implementation using GitLab.

Data Engineering

Subject: Azure Cloud Data Lake Development; Azure Big Data Engineering

Data lake development on Microsoft Azure. Data migration from RDBMS and file sources, data loading into Azure Blob storage and Azure SQL. Design and development of big data batch solutions using Data Factory and Databricks. Massive data warehouse development using Azure SQL Data Warehouse.

Create ETL workflows in Data Factory with data factory ETL pipeline monitoring and management .

Batch processing in Azure Databricks using Scala Spark.

Data warehouse design and development using SQL Data Warehouse.

DWH data model design, featuring index and partitioning table design .

Accessing and processing big data in Blob storage via Transact-SQL using Polybase .

CI/CD development using SBT and Gitlab.


Subject: Cloud Data Lake DevOps; Azure DevOps

Data Factory and Databricks provisioning and deployment. Operationalization of cloud data solutions and infrastructure as code (IaC) implementation using ARM templates and Azure Python SDK for resource management. Azure SQL data warehouse provisioning and deployment. CI/CD implementation using Azure DevOps tools. Development, management, and deployment of Docker images and containers.

Azure resources (VM and storage account, SQL DB and network) provisioning using Azure Python SDK and ARM template .

SQL data warehouse provisioning with Databricks and Data Factory integration, using Python scripts and ARM templates, with Azure Key Vault for deployment .

User account and role-based (RBAC) access management in Azure Active Directory.

Docker image development for batch processing applications and ML model APIs, using Azure Container Registry for build, storage, and management of images.

Azure container deployment on ACI (Azure Container Instances).

CI/CD implementation via Azure Repos, Azure Artifacts, Azure Pipelines, and Azure Test Plans.

NLP Project

NLP Project

Industry: AI/Automatization

Subject: Implementation of supervised Machine Learning Algorithm for automatic keyphrase extraction.

Implementation of automated Context Tagger for a B2B Marketing automated AI solution. Text classification models are implemented in Python using Python Text Mining, NLP and other ML and data analysis libraries (Python Data Science and ML stack). Text mining, data processing, and feature engineering of a massive dataset in Spark.

Design and implementation of a very fast multi-threaded AKKA-based stream (SAX/Stax) processing of XML data for transforming huge XML data to CSV format.

Preprocessing of the data by filtering normalizing text content and applying Spacy and NLTK.

Text mining and data preprocessing in Spark SQL Scala on Hadoop and S3.

Training of embedding and language models using fastText, Gensim, and GPT-2.

Multi-Class multi-label text classification using CNN and word embedding models using Keras and PyTorch.

Modeling term to tag relations in massive graph networks in Tigergraph.

Keyphrase extraction (automatic tagging) using N-Grams, Word2vec scoring and PageRank algorithm on massive graphs of tag-to-tag relations .

FP-Growth association rules learning.

Distributed CNN training in Docker containers on AWS using GPU instances.

Recommendation Prediction Model

Recommendation Prediction Model

Industry: E-Commerce

Subject: ML Model implementation for Recommendation Models

User tracking data is used in training ML models for user-profiling, recommendation, and prediction. RNN and CNN models are developed and trained for enrichment of user-profiles. Classification GBM (Gradient Boosting Machine) on extracted and learned features. Workflow implementation for data engineering and continuous model training implementation in Airflow.

Feature engineering using Spark SQL by joining and aggregating user tracking data.

Keras and TensorFlow implementation for training RNN and CNN models.

Using Spark ML for training gradient boosting classifiers .

Cross-validation, F1-score evaluation, hyperparameter optimization .

Containerized Spark standalone cluster, using Docker Compose for local deployment and AWS container services.

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data lake case study aws

Data Management

BI & Analytics

Custom Development Services

Support & Training

Serverless Data Lakes on Amazon Web Services (AWS)

data lake case study aws

Loan Marketplace Harnesses Their Data Using Server-less Data Lakes

WCI Data Solutions was able to help the largest small business loan marketplace in the United States harness their data by designing and developing a serverless data lake service on Amazon Web Services (AWS) with Amazon S3 as the primary storage platform. 

Our customer is the largest small business loan marketplace in the United States. Small business owners utilize their free online service to find financing by browsing multiple loan products from a network of more than 75 lenders. The platform reviews metrics including the business's financial projections, use of funds, industry, and monthly revenue to find the right loan option for borrowers, helping traditionally underserved groups like women and minority-owned small businesses, mom and pop shops, and seasonal businesses. Their matching engine allows small business borrowers the opportunity to comparison shop across a broad range of lenders and loan products, decreasing their time and effort, and optimizing which lenders to send applications to.

Our customer came to us with an interest in the development and deployment of a Data Lake solution on Amazon Web Services . They wanted a system capable of supporting both analysis and reporting across the organization but also designed to accommodate future consumers, query methods, and new data sources. They wanted to begin this development with an internally developed system we’ll refer to as their “CRM”. The CRM application is also deployed in AWS and uses Aurora RDS as a backend. This will be the first data source used by the WCI developed solution, with other sources to follow. The data will be consumed by the organization using both Domo and Power BI .

The engagement required utilizing a broad array of services on AWS, including:

Amazon Elastic Compute Cloud (Amazon EC2), Amazon Relational Database Service (Amazon RDS), Amazon Glue, Amazon Athena, Amazon CloudWatch, Amazon Simple Queue Service (Amazon SQS), AWS Lambda, Amazon Virtual Private Cloud Peering (VPC-Peering), AWS Identity and Access Management (IAM) and Amazon Simple Storage Service (Amazon S3).

Serverless Data Lake Architecture on AWS

WCI assisted the customer in solving this problem by designing and building a serverless data lake service on AWS with Amazon S3 as the primary storage platform. Utilizing an Amazon S3-based data lake architecture allows our customer the means to have a centralized, secure, and highly durable cloud-based storage platform. AWS makes it easy to store data in any format, both securely and at a massive scale with Amazon S3. In an effort to reduce both complexity and cost of development, WCI decided to utilize Amazon Athena as a query service to produce views that could then be used, in conjunction with Domo, to provide valuable business metrics and insights from the CRM data.

With the use of WCI’s solution, our customer now has the ability to scale their data analytics platform as they continue to add data from new sources, regardless of whether it be structured or unstructured data. The new cost-efficient and cloud-based architecture provides business end-users a data analytics process that is both expedient and lends itself to being future proof. Using AWS native services like Athena, Glue and Lambda enables the ability to continue improving data analysis without the burden of maintaining infrastructure or complex ETL processes, and with WCI’s assistance, our customer now has a secured end-to-end framework for their data and analytics practice on AWS.

Our customer now has the ability to harness increased data from various sources and options for future integrations and for use in other business-critical functions. This gives them the capability to analyze their data using different analytics tools and services, which allows them to derive more insights and provide more value for their customers, suppliers, and partners. WCI developed solution allows them the ability to gain deeper insights into their data in ways that traditional data silos cannot, which in turn helps them continue to support their customers better than any other lending platform.

About WCI Data Solutions

WCI is a consulting services company focused on the application of data through the use of Business Intelligence and Data Warehouse technologies that bring increased business performance for our clients. WCI has serviced well over 300 companies through our knowledge and expertise of how to make data valuable to decision-makers.

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