Cloudera

SEPTEMBER 15, 2022

In this blog post, we will talk about a single Ozone cluster with the capabilities of both Hadoop Core File System (HCFS) and Object Store (like Amazon S3). Please refer to our earlier Cloudera blog for more details about Ozone’s performance benefits and atomicity guarantees. FILE_SYSTEM_OPTIMIZED Bucket (“FSO”). LEGACY Bucket.

Systems 86

Systems Hadoop Metadata Telecommunication 86

Cloudera

NOVEMBER 8, 2022

It enables cloud-native applications to store and process mass amounts of data in a hybrid multi-cloud environment and on premises. These could be traditional analytics applications like Spark, Impala, or Hive, or custom applications that access a cloud object store natively. Conclusion.

Metadata 80

Metadata Algorithm Hadoop Cloud 80

Rockset

JANUARY 23, 2024

When building applications on change data capture (CDC) data using Elasticsearch, you’ll want to architect the system to handle frequent updates or modifications to the existing documents in an index. In this blog, we’ll walk through the different options available for updates including full updates, partial updates and scripted updates.

Metadata 52

Metadata Coding Analytics Application Python 52

Cloudera

DECEMBER 7, 2017

Cloud promises many advantages as an environment for machine learning and analytics. Cloud makes it fast and easy to spin up resources for new applications. Cloud offers elasticity of those resources to efficiently support transient analytics workloads and data pipelines. There is a better way.

Building 40

Building Management Government BI 40

Cloudera

SEPTEMBER 10, 2018

Uniquely, Cloudera’s machine learning and analytics platform have a fundamental characteristic called the Shared Data Experience (SDX) that provides just that. When transient cloud infrastructures are used to complement existing on-premises investments, establishing and capturing this data context is essential for success.

Data Warehouse 40

Data Warehouse Metadata Retail Cloud 40

AltexSoft

OCTOBER 21, 2022

A single cluster can span across multiple data centers and cloud facilities. cloud data warehouses — for example, Snowflake , Google BigQuery, and Amazon Redshift. Depending on the type of deployment (cloud or on-premise), cluster size, and the number of integrations, the deployment may take days to weeks to even months.

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. Hadoop can execute MapReduce applications in various languages, including Java, Ruby, Python, and C++. When to use MapReduce with Big Data.

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? Better yet, it works in any cloud environment. Not to worry.

Kafka 84

Kafka Manufacturing Data Lake SQL 84

Cloudera

NOVEMBER 22, 2021

Explosion of data availability from a variety of sources, including on-premises data stores used by enterprise data warehousing / data lake platforms, data on cloud object stores typically produced by heterogenous, cloud-only processing technologies, or data produced by SaaS applications that have now evolved into distinct platform ecosystems (e.g.,

Government 89

Government Hadoop Data Security Data Warehouse 89

phData: Data Engineering

JULY 18, 2023

With the birth of cloud data warehouses, data applications, and generative AI , processing large volumes of data faster and cheaper is more approachable and desired than ever. First up, let’s dive into the foundation of every Modern Data Stack, a cloud-based data warehouse.

Data Warehouse 52

Data Warehouse Pipeline-centric Government Data 52