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Optimizing Cloudera Data Engineering Autoscaling Performance

Cloudera

At Cloudera, we introduced Cloudera Data Engineering (CDE) as part of our Enterprise Data Cloud product — Cloudera Data Platform (CDP) — to meet these challenges. In future blogs we will explore larger scale tests to profile the performance and efficiency benefits at 500+ nodes. fixed sized clusters).

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Hire Big Data Engineer: Salaries, Stack and Roles

Mobilunity

Technologies that have expanded Big Data possibilities even further are cloud computing and graph databases. The cloud offers excellent scalability, while graph databases offer the ability to display incredible amounts of data in a way that makes analytics efficient and effective. Who is Big Data Engineer?

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Unlocking the Power of AI with a Real-Time Data Strategy

CIO

This has also accelerated the execution of edge computing solutions so compute and real-time decisioning can be closer to where the data is generated. AI continues to transform customer engagements and interactions with chatbots that use predictive analytics for real-time conversations. report they have established a data culture 26.5%

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Cloudera Customer Story

Cloudera

Implementing a cohesive data vision LGIM’s Global Chief Operating Office is responsible for the company’s technology, data, client servicing and the management of its strategic operating agenda. This includes delivering a transformation project to modernize LGIM, enable growth, and introduce more scalable and seamless services.

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Don’t Blink: You’ll Miss Something Amazing!

Cloudera

We can move to predictive fraud and breach prevention, greatly increasing the protection of customer data and financial assets. Without real-time analytics we won’t catch the threats until after they’ve caused significant damage. We can also benefit from real-time stock ticker analytics, and other highly monetizable data assets.

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Happy Birthday, CDP Public Cloud

Cloudera

Data Warehouse – in addition to a number of performance optimizations, DW has added a number of new features for better scalability, monitoring and reliability to enable self-service access with security and performance . Enrich – Data Engineering (Apache Spark and Apache Hive). New Services.

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Machine Learning with Python, Jupyter, KSQL and TensorFlow

Confluent

Building a scalable, reliable and performant machine learning (ML) infrastructure is not easy. It takes much more effort than just building an analytic model with Python and your favorite machine learning framework. It allows real-time data ingestion, processing, model deployment and monitoring in a reliable and scalable way.