Popular Brands that use Data Pipelines

From the recommendation engine guiding you to your next binge-worthy show to the perfect playlist tailored to match your mood, some of the most innovative tech brands are driven by data pipelines quietly working their magic behind the scenes.

Here are a few examples of how tech giants extract value from data pipelines:

1. JP Morgan Chase's Utilization of Big Data Analytics:
   JP Morgan Chase consistently generates massive big data related to customers from credit card information and millions of transactions.

To efficiently process complex unstructured data, the company implemented a robust data pipeline using Hadoop and AWS cloud services. They embraced Hadoop for robust big data analytics, enabling the efficient processing of large datasets.

Currently, JP Morgan Chase uses big data to optimize sales of foreclosed properties, develop marketing initiatives, manage risks, and assess credit. The analytics technology crunches massive customer data to identify patterns in the financial market and customer behavior, helping the bank identify risks and opportunities.

2. Netflix's Strategy for Building and Scaling Data Lineage:
   Netflix faced a highly complex data environment with various systems and teams sharing information. To map the connections between these systems, they initiated a data lineage project.

The project pulls in metadata from various Netflix data platforms and jobs, utilizing systems like Inviso, Lipstick, Spark, Snowflake, and Meson to gather lineage information. Additional context comes from metadata databases like Metacat and Genie.

All this metadata feeds into a unified model capturing entities, relationships, and details about the data. This structured information is stored in graph databases and a data warehouse, powering search and visibility interfaces via APIs in graphical, SQL, and REST formats.

The Big Data Portal utilizes lineage data to showcase data flows between systems, improve search, and display how upstream sources impact downstream jobs. Other applications include monitoring service performance, controlling costs, and improving reliability.

Looking ahead, Netflix is focused on integrating more systems, using advanced compute solutions like Spark, building additional APIs, and connecting lineage information with data quality scanning tools, with the ultimate goal of achieving full visibility into how all of Netflix's data moves through their cloud environment.

3. IBM's Approach to Scaling Customer Data Foundation and Boosting Revenue:
   IBM, with a plethora of offerings on its cloud platform, sought a solution to unlock the platform's total value hindered by the challenge of cross-selling relevant products. Consequently, IBM aimed to implement a solution that could occasionally trigger notifications about its latest offerings to customers.

IBM Cloud streamlined customer data management with Twilio Segment, optimizing data processes across product lines and seamlessly integrating analytics tools. The team leverages Watson Studio and Watson Natural Language Classifier for efficient AI-driven customer insights, reducing time and resource investments.

Deploying Twilio Segment on IBM Cloud captures user behavior for a product. Data is then directed to Amplitude for analysis by the product and growth teams and to Salesforce for enhanced sales visibility. Leveraging insights, they identified disengagement areas and upsell opportunities.

Post Twilio Segment integration, IBM observed a 30% improvement in the adoption of cloud products. Over a three-month period, there was a surge of around 70% in its overall revenue. Additionally, IBM saw a 17% increase in billable usage and a 10x return on its Twilio investment.

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