A growing number of enterprises are pooling terabytes and petabytes of data, but many of them are grappling with ways to apply their big data as it grows.
How can companies determine what big data solutions will work best for their industry, business model, and specific data science goals?
Check out these big data enterprise case studies from some of the top big data companies and their clients to learn about the types of solutions that exist for big data management.
Enterprise case studies
- Netflix on AWS
- AccuWeather on Microsoft Azure
- China Eastern Airlines on Oracle Cloud
- Etsy on Google Cloud
- mLogica on SAP HANA Cloud
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Netflix is one of the largest media and technology enterprises in the world, with thousands of shows that its hosts for streaming as well as its growing media production division. Netflix stores billions of data sets in its systems related to audiovisual data, consumer metrics, and recommendation engines. The company required a solution that would allow it to store, manage, and optimize viewers’ data. As its studio has grown, Netflix also needed a platform that would enable quicker and more efficient collaboration on projects.
“Amazon Kinesis Streams processes multiple terabytes of log data each day. Yet, events show up in our analytics in seconds,” says John Bennett, senior software engineer at Netflix.
“We can discover and respond to issues in real-time, ensuring high availability and a great customer experience.”
Industries: Entertainment, media streaming
Use cases: Computing power, storage scaling, database and analytics management, recommendation engines powered through AI/ML, video transcoding, cloud collaboration space for production, traffic flow processing, scaled email and communication capabilities
- Now using over 100,000 server instances on AWS for different operational functions
- Used AWS to build a studio in the cloud for content production that improves collaborative capabilities
- Produced entire seasons of shows via the cloud during COVID-19 lockdowns
- Scaled and optimized mass email capabilities with Amazon Simple Email Service (Amazon SES)
- Netflix’s Amazon Kinesis Streams-based solution now processes billions of traffic flows daily
Read the full Netflix on AWS case study here.
AccuWeather is one of the oldest and most trusted providers of weather forecast data. The weather company provides an API that other companies can use to embed their weather content into their own systems. AccuWeather wanted to move its data processes to the cloud. However, the traditional GRIB 2 data format for weather data is not supported by most data management platforms. With Microsoft Azure, Azure Data Lake Storage, and Azure Databricks (AI), AccuWeather was able to find a solution that would convert the GRIB 2 data, analyze it in more depth than before, and store this data in a scalable way.
“With some types of severe weather forecasts, it can be a life-or-death scenario,” says Christopher Patti, CTO at AccuWeather.
“With Azure, we’re agile enough to process and deliver severe weather warnings rapidly and offer customers more time to respond, which is important when seconds count and lives are on the line.”
Industries: Media, weather forecasting, professional services
Use cases: Making legacy and traditional data formats usable for AI-powered analysis, API migration to Azure, data lakes for storage, more precise reporting and scaling
- GRIB 2 weather data made operational for AI-powered next-generation forecasting engine, via Azure Databricks
- Delta lake storage layer helps to create data pipelines and more accessibility
- Improved speed, accuracy, and localization of forecasts via machine learning
- Real-time measurement of API key usage and performance
- Ability to extract weather-related data from smart-city systems and self-driving vehicles
Read the full AccuWeather on Microsoft Azure case study here.
China Eastern Airlines is one of the largest airlines in the world that is working to improve safety, efficiency, and overall customer experience through big data analytics. With Oracle’s cloud setup and a large portfolio of analytics tools, it now has access to more in-flight, aircraft, and customer metrics.
“By processing and analyzing over 100 TB of complex daily flight data with Oracle Big Data Appliance, we gained the ability to easily identify and predict potential faults and enhanced flight safety,” says Wang Xuewu, head of China Eastern Airlines’ data lab.
“The solution also helped to cut fuel consumption and increase customer experience.”
Industries: Airline, travel, transportation
Use cases: Increased flight safety and fuel efficiency, reduced operational costs, big data analytics
- Optimized big data analysis to analyze flight angle, take-off speed, and landing speed, maximizing predictive analytics for engine and flight safety
- Multi-dimensional analysis on over 60 attributes provides advanced metrics and recommendations to improve aircraft fuel use
- Advanced spatial analytics on the travelers’ experience, with metrics covering in-flight cabin service, baggage, ground service, marketing, flight operation, website, and call center
- Using Oracle Big Data Appliance to integrate Hadoop data from aircraft sensors, unifying and simplifying the process for evaluating device health across an aircraft
- Central interface for daily management of real-time flight data
Read the full China Eastern Airlines on Oracle Cloud case study here.
Etsy is an e-commerce site for independent artisan sellers. With its goal to create a buying and selling space that puts the individual first, Etsy wanted to advance its platform to the cloud to keep up with needed innovations. But it didn’t want to lose the personal touches or values that drew customers in the first place. Etsy chose Google for cloud migration and big data management for several primary reasons: Google’s advanced features that back scalability, its commitment to sustainability, and the collaborative spirit of the Google team.
Mike Fisher, CTO at Etsy, explains how Google’s problem-solving approach won them over.
“We found that Google would come into meetings, pull their chairs up, meet us halfway, and say, ‘We don’t do that, but let’s figure out a way that we can do that for you.'”
Industries: Retail, E-commerce
Use cases: Data center migration to the cloud, accessing collaboration tools, leveraging machine learning (ML) and artificial intelligence (AI), sustainability efforts
- 5.5 petabytes of data migrated from existing data center to Google Cloud
- >50% savings in compute energy, minimizing total carbon footprint and energy usage
- 42% reduced compute costs and improved cost predictability through virtual machine (VM), solid state drive (SSD), and storage optimizations
- Democratization of cost data for Etsy engineers
- 15% of Etsy engineers moved from system infrastructure management to customer experience, search, and recommendation optimization
Read the full Etsy on Google Cloud case study here.
mLogica is a technology and product consulting firm that wanted to move to the cloud, in order to better support its customers’ big data storage and analytics needs. Although it held on to its existing data analytics platform, CAP*M, mLogica relied on SAP HANA Cloud to move from on-premises infrastructure to a more scalable cloud structure.
“More and more of our clients are moving to the cloud, and our solutions need to keep pace with this trend,” says Michael Kane, VP of strategic alliances and marketing, mLogica
“With CAP*M on SAP HANA Cloud, we can future-proof clients’ data setups.”
Industry: Professional services
Use cases: Manage growing pools of data from multiple client accounts, improve slow upload speeds for customers, move to the cloud to avoid maintenance of on-premises infrastructure, integrate the company’s existing big data analytics platform into the cloud
- SAP HANA Cloud launched as the cloud platform for CAP*M, mLogica’s big data analytics tool, to improve scalability
- Data analysis now enabled on a petabyte scale
- Simplified database administration and eliminated additional hardware and maintenance needs
- Increased control over total cost of ownership
- Migrated existing customer data setups through SAP IQ into SAP HANA, without having to adjust those setups for a successful migration
Read the full mLogica on SAP HANA Cloud case study here.