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Data Pipelines

Building Secure and Scalable Healthcare Data Pipelines

The healthcare industry is beginning to digitally transform with its adoption of continuously advancing technologies. Healthcare organizations are moving toward a more connected and collaborative healthcare ecosystem for improving the way they provide care. Any data-driven organization knows the importance of high-quality data pipelines in data science. The large amounts of heterogeneous healthcare data, call for healthcare organizations to adopt data-driven approaches to automate medical workflows.

What Is a Data Pipeline and Why Your Ecommerce Business Needs One

Our six key points on data pipelines include: Whether you’re a one-person show reselling items on an online marketplace or a large Ecommerce enterprise with hundreds of employees, these businesses share a common factor: both generate data. The size of your business can influence the amount of data you generate, sure. But any amount of data — if it’s not adequately accessible — is worthless. Every business, especially an Ecommerce business, needs a data pipeline.

Build Hybrid Data Pipelines and Enable Universal Connectivity With CDF-PC Inbound Connections

In the second blog of the Universal Data Distribution blog series, we explored how Cloudera DataFlow for the Public Cloud (CDF-PC) can help you implement use cases like data lakehouse and data warehouse ingest, cybersecurity, and log optimization, as well as IoT and streaming data collection. A key requirement for these use cases is the ability to not only actively pull data from source systems but to receive data that is being pushed from various sources to the central distribution service.

Modernizing the Analytics Data Pipeline

Enterprises run on a steady flow of best-fit data analytics. Robust processes ensure these assets are always accurate, relevant, and fit for purpose. Increasingly, organizations are implementing these processes within structured development and operationalization “pipelines.” Typically, analytics data pipelines include data engineering functions such as extract-transform-load (ETL) and data science processes such as machine-learning model development.