Cloudera

FEBRUARY 9, 2021

This ‘need for speed’ drives a rethink on building a more modern data warehouse solution, one that balances speed with platform cost management, performance, and reliability. In this way, the analytic applications are able to turn the latest data into instant business insights. Low Maintenance.

Data Warehouse 69

Data Warehouse Cloud Kafka Cloud Storage 69

Cloudera

SEPTEMBER 15, 2022

A key area of focus for the symposium this year was the design and deployment of modern data platforms. The third element in the process is the connection between the data products and the collection of analytics applications to provide business results.

Data Lake 79

Data Lake Analytics Application Cloud Storage Architecture 79

AltexSoft

OCTOBER 21, 2022

popular SQL and NoSQL database management systems including Oracle, SQL Server, Postgres, MySQL, MongoDB, Cassandra, and more; cloud storage services — Amazon S3, Azure Blob, and Google Cloud Storage; message brokers such as ActiveMQ, IBM MQ, and RabbitMQ; Big Data processing systems like Hadoop ; and. You can find off-the-shelf links for.

Kafka 93

Kafka Hadoop ETL Tools Big Data 93

Cloudera

FEBRUARY 8, 2024

Overview This blog post describes support for materialized views for the Iceberg table format. Apache Iceberg is a high-performance open table format for petabyte-scale analytic datasets. Cloudera Data Warehouse (CDW) running Hive has previously supported creating materialized views against Hive ACID source tables.

Metadata 88

Metadata Data Warehouse BI AWS 88

ProjectPro

JANUARY 31, 2023

If you're looking to break into the exciting field of big data or advance your big data career, being well-prepared for big data interview questions is essential. Get ready to expand your knowledge and take your big data career to the next level! But the concern is - how do you become a big data professional?

Big Data 52

Big Data Hadoop AWS Relational Database 52

Cloudera

SEPTEMBER 10, 2018

With the release of SDX for Altus workloads as-a-service, we’re now supporting the second most common combination: sharing data and metadata between customers’ own Cloudera workloads deployed to the public cloud (IaaS) with Altus Director and those managed in the public cloud by Cloudera as a service (Altus PaaS).

Data Warehouse 40

Data Warehouse Metadata Retail Cloud 40

Cloudera

MARCH 6, 2018

Instead, they have separate data stores and inconsistent (if any) frameworks for data governance, management, and security. This leads to extra cost, effort, and risk to stitch together a sub-optimal platform for multi-disciplinary, cloud-based analytics applications.

Cloud 40

Cloud Metadata Big Data AWS 40