data lake case study aws

Building a Data Lake on AWS: A Comprehensive Guide

by Brad Winett | Jun 8, 2023 | White Papers

Introduction

Definition and components of a data lake, key features and advantages of aws data lakes, common use cases for data lakes on aws, defining objectives and goals, identifying data sources and types, architectural considerations and design patterns, evaluating and selecting aws services for the data lake, creating an aws account and configuring security settings, setting up amazon s3 for data storage , configuring aws glue for data cataloging and etl, integrating amazon athena for serverless querying, optional: adding amazon emr for big data processing , data ingestion methods and best practices , extracting, transforming, and loading (etl) data with aws glue , managing data catalog and metadata with aws glue data catalog , data governance and access control best practices .

• Querying Data with Amazon Athena 

Leveraging Amazon QuickSight for Data Visualization 

Advanced analytics and machine learning on aws, data lake monitoring best practices .

• Data Lake Performance Optimization Techniques 

Backup and Disaster Recovery Strategies

Security and compliance considerations , data lake integration with other aws services , real-time streaming and iot data in the data lake .

• Conclusion 

About TrackIt

A data lake is a centralized repository that allows users to store and analyze vast amounts of structured, semi-structured, and unstructured data in its raw form. Unlike traditional data warehouses, data lakes retain data in its original format until it’s required for analysis. This flexibility enables businesses to perform advanced analytics, gain actionable insights, and drive data-driven decision-making.

AWS (Amazon Web Services) provides a comprehensive suite of services that assist in building robust and scalable data lakes on the cloud. This range of services includes storage, data processing, cataloging, analytics, and visualization, making it an ideal platform for building and managing data lakes. Below is a detailed guide that covers various aspects of building a data lake on AWS, from architecture and planning to setting up, ingesting, and managing data. The guide aims to provide readers with a thorough understanding of how to leverage AWS services to build and maintain a reliable data lake.

Understanding Data Lakes

Data lakes consist of three key components: data storage, data catalog, and data analysis. The data storage component typically consists of using  Amazon Simple Storage Service (S3)  for storing data in its raw format. The data catalog component is usually powered by  AWS Glue , a data integration service that helps catalog and prepare data for analysis. The data analysis component often includes services like  Amazon Athena  and  Amazon Elastic MapReduce (EMR)  used for efficient querying, analytics, and processing of data.

AWS provides several key features that make it an ideal platform for data lake implementations. These features include scalability, durability, cost-effectiveness, flexibility, security, and seamless integration with other AWS services. Building data lakes on AWS allows companies to handle large volumes of data, ensure data durability through redundancy, and optimize costs by taking advantage of AWS’s pay-as-you-go pricing model. 

Common use cases for data lakes include data analytics, business intelligence, machine learning, IoT data analysis, log analysis, fraud detection, and customer behavior analysis. Data lakes provide valuable business insights and drive innovation by ingesting, processing, and analyzing diverse data types from multiple sources. 

Planning Your Data Lake on AWS

It is essential to clearly define objectives and goals before building a data lake on AWS. These can include improving data accessibility, enabling self-service analytics, accelerating time-to-insights performance, facilitating data-driven decision-making, and fostering innovation within the organization. Defining clear goals assists in making informed decisions during the planning and implementation phases.

Proper identification of data sources and data types to be ingested into the data lake is crucial. Data sources can include transactional databases, log files, streaming data, social media feeds, sensor data, and more. Understanding the different data types and formats such as structured, semi-structured, or unstructured, helps in the selection of appropriate AWS services for ingestion, processing, and analysis.

Architectural considerations play a vital role in the success of a data lake implementation. The following factors need to be taken into account: 

• Data ingestion patterns 
• Data transformation requirements
• Data access patterns
• Security and compliance requirements 
• Integration with existing systems

AWS provides  architectural design patterns and principles  that can guide companies in designing a robust and scalable data lake architecture.

AWS offers a diverse array of services that can be leveraged to build a data lake. The selection of services is often reliant on the specific requirements of the implementation. Services such as Amazon S3 for data storage, AWS Glue for data cataloging and ETL, Amazon Athena for serverless querying, and AWS EMR for big data processing are commonly used in data lake implementations. Evaluating and selecting the right combination of services is essential for a successful data lake deployment.

Setting Up Your AWS Data Lake

To begin setting up the AWS data lake, an AWS account is required. During the account setup, it is crucial to configure appropriate security settings, including IAM (Identity and Access Management) policies, security groups, encryption options, and network settings. Security best practices should be followed to ensure data protection and compliance with industry standards.

Amazon S3  serves as the primary data storage layer for the data lake. Essential steps in the setup process include: 

• Creating an S3 bucket
• Defining the appropriate access controls
• Encryption settings
• Versioning options

Amazon S3 provides high scalability, durability, and availability, making it an ideal choice for storing large volumes of data.

AWS Glue  is a fully-managed extract, transform, and load (ETL) service that simplifies the process of cataloging and preparing data for analysis. Setting up AWS Glue involves four steps:

• Step 1: Creating a data catalog
• Step 2: Defining crawler configurations to automatically discover and catalog data
• Step 3: Creating and running ETL jobs
• Step 4: Managing metadata

AWS Glue enables the transformation of raw data into a queryable and analyzable format.

Amazon Athena  is a serverless query service that helps analyze data stored in S3 using standard SQL queries. Setting up Amazon Athena requires three steps: 

• Step 1: Defining the database and table schemas (not required if a crawler was run as specified in the previous section)
• Step 2: Configuring query result locations
• Step 3: Granting appropriate permissions for accessing data

Amazon Athena provides a convenient way to interactively query data stored in the data lake without the need for infrastructure provisioning or management.

For scenarios requiring complex data processing,  Amazon EMR (Elastic MapReduce)  can be integrated into the data lake architecture. Amazon EMR provides a managed big data processing framework that supports popular processing engines such as Apache Spark and Apache Hadoop. Setting up Amazon EMR requires three steps: 

• Step 1: Defining cluster configurations
• Step 2: Launching and managing clusters
• Step 3: Executing data processing jobs at scale

Ingesting and Managing Data in the Data Lake

Ingesting data into the data lake can be achieved through several methods, including batch ingestion, streaming ingestion, and direct data integration. AWS provides services such as  AWS Data Pipeline ,  AWS Glue ,  Amazon AppFlow , and  AWS Kinesis  to facilitate data ingestion. Best practices for data ingestion include data validation, data compression, error handling, and monitoring.

AWS Glue  simplifies the ETL process by automating the extraction, transformation, and loading of data from multiple sources. Glue jobs can be created to do the following: 

• Transform raw data into a desired format
• Apply data cleansing and enrichment
• Load transformed data into the data lake. 

AWS Glue also provides visual tools and pre-built transformations that simplify the process of building scalable and efficient ETL workflows.

The  AWS Glue Data Catalog  acts as a centralized metadata repository for the data lake. It stores metadata information such as table definitions, schema details, and data partitions. Managing the data catalog requires two steps: 

• Step 1: Configuring metadata databases, tables, and partitions
• Step 2: Ensuring data catalog integrity and consistency

The data catalog enables users to easily discover, explore, and analyze data within the data lake.

Data governance and access control are critical to data lake management. AWS provides several mechanisms for implementing data governance including:

• IAM policies
• S3 bucket policies
• AWS Glue security configurations

Additionally,  AWS Lake Formation  can play a pivotal role in managing resources and permissions associated with the data lake. Lake Formation simplifies data lake management by providing comprehensive control and oversight. The service helps establish and enforce data access policies, define fine-grained permissions, and manage resource-level permissions efficiently. 

One powerful feature offered by AWS Lake Formation is the ability to assign LF (Lake Formation) tags to specific columns or tables. These tags enable partial access control, allowing companies to grant or restrict access based on user requirements. For example, User A can access all tables except the columns labeled with the “sensitive” LF tag. This granular access control provides enhanced data security.

In addition to data governance and access control, implementing encryption mechanisms can also be prioritized to ensure adherence to data privacy regulations.

Analyzing and Visualizing Data in the Data Lake 

Querying data with amazon athena  .

Amazon Athena  enables the querying of data stored in Amazon S3 using standard SQL queries. Users can create tables, define data schemas, and run ad hoc queries against the data lake. Amazon Athena supports multiple data formats, including CSV, JSON, Parquet, and Apache Avro. Query results can be exported to various formats or integrated with other AWS services for further analysis.

Amazon QuickSight  is a business intelligence and data visualization service that integrates seamlessly with data lakes on AWS. It allows users to create interactive dashboards, visualizations, and reports using data from the data lake. Setting up Amazon QuickSight requires connecting to the data lake as a data source, defining data transformations, and creating visualizations using a drag-and-drop interface.

AWS provides a range of advanced analytics and machine learning services that can be integrated with the data lake for more sophisticated analysis. The following services can be leveraged: 

• Amazon Redshift : Data warehouse used to efficiently store and organize large volumes of data. Redshift can be used to perform complex queries and analyses of data.
• Amazon SageMaker : Cloud machine-learning platform used to build, train, and deploy machine-learning models using the data in the data lake. The trained models help extract valuable insights, make predictions, and automate decision-making processes.
• Amazon Forecast :  Time-series forecasting service used to generate accurate forecasts and predictions from historical data stored in the data lake. These forecasts can help businesses optimize inventory management, demand planning, and resource allocation.

Data Lake Maintenance and Monitoring 

Monitoring the health and performance of a data lake is crucial to ensuring uninterrupted service. AWS provides services like  Amazon CloudWatch ,  AWS CloudTrail , and  AWS Glue DataBrew  for monitoring various aspects of the data lake, including resource utilization, data quality, job executions, and data lineage. Implementing proactive monitoring practices helps in detecting issues and optimizing the data lake’s performance.

Data Lake Performance Optimization Techniques  

To achieve optimal performance, companies can employ established techniques such as partitioning data, optimizing query performance, and using appropriate compression formats.  AWS Glue DataBrew  can be used to profile and optimize data quality and structure. Properly configuring and tuning the data lake components and leveraging AWS best practices can significantly enhance overall performance. Files can also be converted into columnar formats such as Parquet or Avro to reduce the number of files to be scanned for analysis and enable cost optimization.

Backup and disaster recovery strategies protect the data lake from data loss and ensure business continuity. AWS provides services and features such as  AWS Backup  and AWS S3 versioning to create automated backup schedules, define retention policies, and restore data in case of disasters or accidental deletions. 

Ensuring data lake security and compliance is critical for any organization running business-critical workloads in the cloud. The following AWS security best practices can be followed to ensure security: 

• Implementing encryption mechanisms for data at rest and in transit
• Enabling audit logging
• Regularly updating security configurations. 

Compliance requirements such as GDPR or HIPAA should also be considered and addressed to ensure data privacy and regulatory compliance within the data lake.

Additional Data Lake Concepts and Strategies 

AWS provides a vast ecosystem of services that can be integrated with the data lake to extend its capabilities. Integration with services like  AWS Lambda  for serverless computing and  AWS Step Functions  for workflow orchestration helps build more sophisticated data processing workflows and enhance data lake functionality.

Note:  As of June 2023, AWS Step Functions are not well-integrated with AWS Glue. It is currently recommended to use Glue workflows for workflow orchestration.

Integrating real-time streaming data and IoT data into the data lake opens up new possibilities for real-time analytics and insights. AWS services such as  Amazon Kinesis  and  AWS IoT Core  facilitate the ingestion and processing of streaming and IoT data. Combining batch and streaming data helps derive valuable real-time insights from the data lake.

Conclusion  

Building a data lake on AWS helps unlock the value of data, gain actionable insights, and drive innovation. However, the process of building a data lake on AWS requires thorough planning, architectural considerations, and choosing the right combination of AWS services. Following the comprehensive guide outlined in this article allows companies to take the first steps toward building a robust and scalable data lake on AWS.

Implementing a data lake on AWS can be a complex endeavor that requires expertise in data analytics workflows, architectural design, and AWS services. To ensure a smooth and successful implementation, it is advisable for companies to partner with an AWS Partner like TrackIt that has deep expertise in building data lakes and implementing data analytics solutions. 

TrackIt can provide guidance throughout the entire process, from planning and architecture design to implementation and ongoing maintenance. TrackIt’s experience and knowledge in working with AWS services and data analytics workflows can significantly accelerate the development of a robust and efficient data lake. 

TrackIt  is an Amazon Web Services Advanced Tier Services Partner specializing in cloud management, consulting, and software development solutions based in Marina del Rey, CA. 

TrackIt specializes in Modern Software Development, DevOps, Infrastructure-As-Code, Serverless, CI/CD, and Containerization with specialized expertise in Media & Entertainment workflows, High-Performance Computing environments, and data storage.

In addition to providing cloud management, consulting, and modern software development services, TrackIt also provides an open-source AWS cost management tool that allows users to optimize their costs and resources on AWS.

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How to Build Data Lake Architecture, with 8 Examples (on S3)

• June 9, 2021

Table of Contents

This article presents reference architectures and examples of data lake implementations, from around the web as well as from Upsolver customers. For a more detailed, hands-on example of building a data lake to store, process and analyze petabytes of data, check our data lake webinar with ironSource and Amazon Web Services . 

Upsolver’s newest offering, SQLake, takes advantage of the same cloud-native processing engine used by the Upsolver customers featured here. With SQLake you can build and run reliable data pipelines on streaming and batch data via an all-SQL experience. Try it for free . No credit card required.

So you’ve decided it’s time to overhaul your data architecture . What’s next? How do you go about building a data lake that delivers the results you’re expecting?

Well, we’re strong believers in the notion that an example is worth a thousand convoluted explanations. That’s why we’ll jump right into real-life examples of companies that have built their data lakes on Amazon S3, after covering some basic principles of data lake architecture. Use this guide for inspiration, reference, or as your gateway to learn more about the different components you’ll need to become familiar with for your own initiative.

What is data lake architecture?

A data lake is an architecture pattern rather than a specific platform, built around a  big data repository that uses a schema – on – read approach . In a data lake, we store large amounts of unstructured data in an object store such as Amazon S3, without structuring the data in advance and while maintaining flexibility to perform further ETL and ELT on the data in the future . This makes it ideal for businesses that need to analyze data that is constantly changing, or very large datasets .

Data lake architecture is simply the combination of tools used to build and operationalize this type of approach to data – starting from event processing tools, through ingestion and transformation pipelines, to analytics and query tools. As we shall see in the examples below, there are many different combinations of these tools that can be used to build a data lake, based on the specific skillset and tooling available in the organizatino.

To learn more, check out What is a Data Lake? and Understanding Data Lakes and Data Lake Platforms .

Design Principles and Best Practices for Building a Data Lake

We’ve covered design principles and best practices in more depth elsewhere – you can check out the links to dive deeper. In this article, we’ll quickly run through the 10 most important factors when building a data lake:

• Event sourcing: store all incoming events in an immutable log, which log can then be used for ETL jobs and analytics use cases. This approach has many benefits, including the ability to reduce costs, validate hypotheses retroactively, and trace issues with processed data.
• Storage in open file formats : A data lake should store data in open formats such as Apache Parquet ,  retain historical data , and use a central meta – data repository . This will enable ubiquitous access to the data and reduce operational costs .
• Optimize for performance : You’ll eventually want to put the data in your lake to use. Store your data in a way that makes it easy to query , using column ar file formats and keeping files to a manageable size . You also need to partition your data efficiently so that queries only retrieve the relevant data .
• Implement data governance and access control : Tools such as AWS data lake formation make it easier to control access to data in a data lake and address security concerns.
• Schema visibility : You should have the ability to understand the data as it is being ingested in terms of the schema of each data source, sparsely populated fields, and metadata properties. Gaining this visibility on read rather than trying to infer it on write will save you a lot of trouble down the line by enabling you to build ETL pipelines based on the most accurate and available data.

Learn more about how to structure your data lake .

8 Data Lake Examples to Copy and Learn From

Data lakes are used to power data science and machine learning, lower the total cost of ownership, simplify data management, incorporate artificial intelligence and machine learning, speed up analytics, improve security and governance, centralize and consolidate data, quickly and seamlessly integrate diverse data sources and formats, and democratize data by offering users broader access to data through more diverse tooling.

Check out the examples below to see how are data lakes are used across industries to reduce costs and improve business agility:

1. Sisense Builds a Versatile Data Lake with Minimal Engineering Overhead

As a leading global provider of business intelligence software, Sisense has data-driven decision making embedded in its DNA. One of the richest sources of data the company has to work with is product usage logs, which capture all manner of users interacting with the Sisense server, the browser, and cloud-based applications.

Over time, and with the rapid growth in Sisense’s customer base, this data had accumulated to more than 70bn records. In order to effectively manage and analyze this data, the company quickly realized it would have to use a data lake architecture, and decided to build one using the AWS ecosystem.  We’ve written a more detailed case study about this architecture, which you can read here .

The Data Lake

To quickly generate value for the business and avoid the complexities of a Spark/Hadoop-based project, Sisense’s CTO Guy Boyangu opted for a solution based on Upsolver, S3, and Amazon Athena.

Product logs are streamed via Amazon Kinesis and processed using Upsolver, which then writes columnar CSV and Parquet files to S3. These are used for visualization and business intelligence using Sisense’s own software. Additionally, structured tables are sent to Athena to support ad-hoc analysis and data science use cases.

To learn more about Sisense’s data lake architecture, check out the case study .

2. Depop Goes From Data Swamp to Data Lake

Depop is a peer-to-peer social shopping app based in London, serving thousands of users. These users take various actions in the app – following, messaging, purchasing and selling products, and so on – creating a constant stream of events.

The Depop team documented their journey in two excellent blog posts . After an initial attempt to create replicas of the data on Redshift, they quickly realized that performance tuning and schema maintenance on Redshift would prove highly cumbersome and resource intensive. This led Depop to adopt a data lake approach using Amazon S3.

The data lake at Depop consists of three different pipelines:

• Ingest: Messages are written via RabbitMQ, and dispatched via a fanout lambda function.
• Fanout: The lambda function sets up the relevant AWS infrastructure based on event type and creates an AWS Kinesis stream.
• Transform: The final step is creating columnar Parquet files from the raw JSON data, and is handled using the AWS Glue ETL and Crawler. From there data is outputted to Athena for analysis.

For more information about Depop’s data lake, check out their blog on Medium .

3. ironSource Streams its Way to Hypergrowth

ironSource is the leading in-app monetization and video advertising platform. ironSource includes the industry’s largest in-app video network. The company collects, stores, and prepares vast amounts of streaming data from millions of end devices. 

When ironSource’s rapid data growth effectively obsoleted its previous system, it chose Upsolver to help it adopt a streaming data lake architecture, including storing raw event data on object storage.

• Using Upsolver’s no-code self-service UI, ironSource ingests Kafka streams of up to 500K events per second, and stores the data in S3. 
• Upsolver automatically prepares data for consumption in Athena, including compaction, compression, partitioning, and creating and managing tables in the AWS Glue Data Catalog. 
• ironSource’s BI teams use Upsolver to enrich and filter data and write it to Redshift to build reporting dashboards in Tableau and send tables to Athena for ad-hoc analytic queries.  

Learn more about ironSource’s data lake architecture .

4. Bigabid Builds a High-Performance Real-time Architecture with Minimal Data Engineering

Bigabid brings performance-based advertisements to app developers, so clients only pay when new users come to their application through the ad.  Bigabid takes advantage of machine learning (ML) for predictive decision-making and goes beyond social media and search engine data sourcing to create an in-depth customer user profile.

Bigabid had to replace its daily batch processing with real-time stream processing, so the company could update user profiles based on users’ most recent actions and continue to develop and scale ad campaigns. Using Upsolver’s visual no-code UI, Bigabid built its streaming architecture so quickly, it saved the equivalent of 6-12 months of engineering work from four dedicated engineers.

Bigabid uses Kinesis Firehose to ingest multiple data streams into its Amazon S3 data lake, then uses Upsolver for data ETL, combining, cleaning, and enriching data from multiple streams to build complete user profiles in real-time.  The company also uses Upsolver and Athena for business intelligence (BI) reporting that is used by its data science team to improve machine learning models. 

Upsolver also automatically prepares data for Athena, optimizing Athena’s storage layer (Parquet format, compaction of small files) and sharply accelerating queries.

Learn more about Bigabid’s real-time data architecture .

5. SimilarWeb Crunches Hundreds of Terabytes of Data

SimilarWeb is a leading market intelligence company that provides insights into the digital world. To provide this service at scale, the company collects massive amounts of data from various sources, which it uses to better understand the way users interact with websites.

In a recent blog post published on the AWS Big Data Blog , Yossi Wasserman from Similar Web details the architecture that the company uses to generate insights from the hundreds of terabytes of anonymous data it collects from mobile sources.

Image source: AWS blog

The SimilarWeb solution utilizes S3 as its events storage layer, Amazon Athena for SQL querying, and Upsolver for data preparation and ETL. In his article, Wasserman details the way data is sent from Kafka to S3, reduced to include only the relevant fields needed for analysis, and then sent as structured tables to Athena for querying and analysis.

Read more about Similar Web’s data lake solution on the AWS blog .

6. An Event-driven, Serverless Architecture at Natural Intelligence

Natural Intelligence runs comparison sites across many different verticals. As Denise Schlesinger details on her blog , the company was looking to accomplish two different goals with this data:

• Query raw, unstructured data for real-time analytics, alerts and machine learning
• Store structured and optimized data for BI and analytics

To effectively work with unstructured data, Natural Intelligence decided to adopt a data lake architecture based on AWS Kinesis Firehose, AWS Lambda, and a distributed SQL engine.

Image source: Denise Schlesinger on Medium

S3 is used as the data lake storage layer into which raw data is streamed via Kinesis. AWS Lambda functions are written in Python to process the data, which is then queried via a distributed engine and finally visualized using Tableau.

For more details about this architecture, check out Denise’s blog on Medium .

7. Peer39 Contextualizes Billions of Pages for Targeting and Analytics

Peer39 is an innovative leader in the ad and digital marketing industry. Each day, Peer39 analyzes more than 450 million unique web pages holistically to contextualize the true meaning of the page text and  topics. It helps advertisers to optimize their spend and place ads in the right place, at the right time.

Upsolver automates and orchestrates Peer39’s data workflow, incorporating built-in data lake best practices including: 

• partitioning
• guaranteed message delivery
• exactly-once processing
• optimization of Athena’s storage layer (Parquet format, compaction of small files) so queries run much faster.

Peer39’s updated streaming data architecture unified teams across data science, analytics, data engineering, and traditional database administration, enabling the company to speed go-to-market with existing staff.

Learn more about Peer39’s streaming data architecture .

8. Browsi – Managing ETL Pipelines for 4 Billion Events with a Single Engineer

Browsi provides an AI-powered adtech solution that helps publishers monetize content by offering advertising inventory-as-a-service. Browsi automatically optimizes ad placements and layout to ensure relevant ad content.

Browsi needed to move from batch processing to stream processing, and move off of manually-coded data solutions.

Browsi implemented Upsolver to replace both the Lambda architecture used for ingest and the Spark/EMR implementation used to process data.

Events generated by scripts on publisher websites are streamed via Amazon Kinesis Streams. Upsolver ingests the data from Kinesis and writes it to S3 while enforcing partitioning, exactly-once processing, and other data lake best practices.

From there, Browsi outputs ETL flows to Amazon Athena, which it uses for data science as well as BI reporting via Domo. End-to-end latency from Kinesis to Athena is now mere minutes.  Meanwhile, a homegrown solution creates internal reports from Upsolver’s daily aggregations of data.

And it’s all managed by a single data engineer.

Learn more about Browsi’s streaming ETL pipelines .

How to Build a Data Lake:

• Map out your structured and unstructured data sources
• Build ingestion pipelines into object storage
• Incorporate a data catalog to identify schema
• Create ETL and ELT pipelines to make data useful for analytics
• Ensure security and access control are managed correctly

Ready to build your own data lake?

Upsolver is the fastest and easiest way to get your S3 data lake from 0 to 1. Schedule a demo to learn how you can go from streams to analytics in a matter of minutes. For additional data infrastructure best practices, check out some of these data pipeline architecture diagrams .

Try SQLake for free for 30 days . SQLake is Upsolver’s newest offering. It lets you build and run reliable data pipelines on streaming and batch data via an all-SQL experience. Try it for free. No credit card required.

As an SEO expert and content writer at Upsolver, Eran brings a wealth of knowledge from his ten-year career in the data industry. Throughout his professional journey, he has held pivotal positions at Sisense, Adaptavist, and Webz.io. Eran's written work has been showcased on well-respected platforms, including Dzone, Smart Data Collective, and Amazon Web Services' big data blog. Connect with Eran on LinkedIn

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Revolutionizing Pharma Analytics with AWS Data Lake

About the Client

The client is a multinational pharmaceutical company based in India. Moreover, the client deals in manufacturing and selling active pharmaceutical ingredients and pharmaceutical formulations in India and the US. Considered as one of the most reputed brands in India, the client has expanded with joint ventures and acquisitions in the last two decades.

The Business Challenge

The client wanted to improve and accelerate analytics-driven decisions and reduce the time for data analysis, data analytics, and data reporting on both structured and unstructured data. Furthermore, the client wanted to improve the deviation tracking of mitigation tasks and reduce the system stack cost by enabling an open-source, industrial-grade platform. In addition, the client also wanted to prepare ground and infra for AI/ML and advanced analytics.

Our Solution

We built enterprise data lake architecture and implemented analytics on the AWS platform. Specifically, this solution included AWS data lake architecture for scalable warehouse and AWS data lake architecture for IoT and unstructured data.

The Impact Aptus Data Labs Made

The enterprise data lake architecture on the AWS platform enabled the client to process, analyze, and report both structured and unstructured data quickly with better analytics-driven decisions. Additionally, this solution helped the client to reduce IT costs and improve business performance.

The Business and Technology Approach

Aptus Data Labs used the following process to build enterprise data lake architecture for scalable warehouse and for IoT and unstructured data to resolve the business challenge. The solution was in three stages. 

• Carried out a detailed requirement and due diligence study
• Understood the client’s technology stack, infrastructure availability & business operation landscape
• Recommended AWS infrastructure/instance, and AWS services considering scalability, performance, and cost
• Created strategies for data migrations and AI/ML business use
• Installed, configured, and tested the instances & services
• Tested the deliverables platform and automated the process
• Followed the PMBOK project management process and CRISP-DM process for the data analytics solution

The Reference Architecture We Built

• AWS Glacier
• AWS Glue, ETL, and Data Catalog
• AWS CloudWatch
• AWS CloudTrail
• AWS DynamoDB
• AWS Quick sight
• Amazon Kinesis
• DbVis Software

The Outcome

The new data architecture based on AWS Cloud benefited the client in multiple ways and helped to resolve the business challenge. The benefits in all the three phases were:

• Advanced analytical capabilities-driven on both structured and unstructured data with Enterprise search enabled for any data
• Machine Learning used to drive improvements and productivity
• Demonstrated connectivity to various databases from Presto
• Backed up email and uploaded data to the cloud
• Uploaded IoT data to the cloud
• Established connectivity from R/Python to Cloud Database/S3 using Libraries
• Enabled Presto/AWS Athena for data search or ad-hoc queries
• Migrated Tableau dashboard to Superset or AWS Quicksights or D3.J3

Related Case Studies

Achieving low-latency API-based queries with Mongo DB

Boosting Performance with Apache Spark Migration

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Implementing a Large AWS Data Lake for Analysis of Heterogeneous Data

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

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

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

Bulk Indexing — Monitoring of Long Time Backend Tasks

Bulk Indexing — Login and Automatic Errors Handling

Multi-tenancy — Agile Access Setup

Metadata — Governance

Cart — Storing and Exploring Results in Personal Cart

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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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

• Reliable storage, with protection against data corruption and other types of data losses
• Security against un-authorized data access
• Ease of finding a single record in billions (by efficiently indexing data)
• An advanced analytics engine that can help them derive and visualise meaningful insights from the client’s high volume and variety of data.
• All of this forming their single source of truth.

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

Completion and reporting

01. understand.

• In collaboration with the client’s development team, we outlined the volume, velocity, veracity and variety of data.
• We worked with the client’s business teams and domain experts to define reports in Power BI for the 18 use cases the client had identified.
• We mapped data to corresponding reports and planned data transformation.
• Based on these, we designed and architected the data lake and pipeline necessary for Power BI.
• With the client’s sign-off, we deployed the solution on AWS cloud.

04. Transform

• Once the infrastructure was in place, our data engineering team performed the necessary ETL steps such as cleaning and consolidation to derive value from the raw data.
• We stored this in an S3 bucket as parquet formatted files.
• We imported transformed data as data-marts into AWS Redshift, to be used for Power BI reports.

05. Completion and reporting

• We delivered a summary of findings and recommendations for production deployment to bring the PoC to a meaningful closure.

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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