Cloudera

NOVEMBER 13, 2017

Cloud PaaS takes this a step further and allows users to focus directly on building data pipelines, training machine learning models, developing analytics applications — all the value creation efforts, vs the infrastructure operations. The net result is much improved productivity for data engineers, data scientists, and analysts.

Cloud 40

Cloud Big Data Metadata Machine Learning 40

Cloudera

DECEMBER 7, 2017

That data may be hard to discover for other users and other applications. Worse, the metadata and context associated with that data may be lost forever if a transient cluster is shut down and the resources released. A way to leverage the benefits of cloud for multi-disciplinary analytics, without all of those problems.

Building 40

Building Management Government BI 40

Cloudera

SEPTEMBER 10, 2018

Multidisciplinary analytics are the tools, the different workloads you need: data engineering, data warehousing, data science, and operational analytics. The company also has a transient Altus Data Engineering workload to bring the data into the Data Warehouse environment.

Data Warehouse 40

Data Warehouse Metadata Retail Cloud 40

AltexSoft

OCTOBER 21, 2022

After trying all options existing on the market — from messaging systems to ETL tools — in-house data engineers decided to design a totally new solution for metrics monitoring and user activity tracking which would handle billions of messages a day. Cloudera , focusing on Big Data analytics. Multi-language environment. ZooKeeper issue.

Kafka 93

Kafka Hadoop ETL Tools Big Data 93

ProjectPro

JANUARY 31, 2023

In this blog, we'll dive into some of the most commonly asked big data interview questions and provide concise and informative answers to help you ace your next big data job interview. NameNode is often given a large space to contain metadata for large-scale files. But the concern is - how do you become a big data professional?

Big Data 52

Big Data Hadoop AWS Relational Database 52

Cloudera

JUNE 16, 2022

This blog aims to answer two questions as illustrated in the diagram below: How have stream processing requirements and use cases evolved as more organizations shift to “streaming first” architectures and attempt to build streaming analytics pipelines?

Kafka 84

Kafka Manufacturing Data Lake SQL 84

phData: Data Engineering

JULY 18, 2023

Common Limitations from Legacy Data Systems Long Turn-Around Time to Set up Infrastructure: Having large on-premise infrastructure results in a setup that is deeply interconnected and often requires an army of engineers to maintain. This typically results in long-running ETL pipelines that cause decisions to be made on stale or old data.

Data Warehouse 52

Data Warehouse Pipeline-centric Government Data 52

Cloudera

NOVEMBER 22, 2021

In addition, data pipelines include more and more stages, thus making it difficult for data engineers to compile, manage, and troubleshoot those analytical workloads. That level of automation and simplicity enables data practitioners to stand up analytical environments in a self-service manner (i.e., CRM platforms).

Government 89

Government Hadoop Data Security Data Warehouse 89