Cloudera

SEPTEMBER 1, 2020

It is designed to simplify deployment, configuration, and serviceability of Solr-based analytics applications. DDE also makes it much easier for application developers or data workers to self-service and get started with building insight applications or exploration services based on text or other unstructured data (i.e.

Cloud Storage 71

Cloud Storage Unstructured Data AWS Analytics Application 71

Cloudera

NOVEMBER 22, 2021

For example, organizations with existing on-premises environments that are trying to extend their analytical environment to the public cloud and deploy hybrid-cloud use cases need to build their own metadata synchronization and data replication capabilities. benchmarking study conducted by independent 3rd party ).

Hadoop 94

Hadoop Government Data Security Cloud 94

Cloudera

MARCH 6, 2018

This leads to extra cost, effort, and risk to stitch together a sub-optimal platform for multi-disciplinary, cloud-based analytics applications. If catalog metadata and business definitions live with transient compute resources, they will be lost, requiring work to recreate later and making auditing impossible.

Cloud 40

Cloud Metadata Big Data Analytics Application 40

Cloudera

FEBRUARY 9, 2021

A typical approach that we have seen in customers’ environments is that ETL applications pull data with a frequency of minutes and land it into HDFS storage as an extra Hive table partition file. In this way, the analytic applications are able to turn the latest data into instant business insights.

Data Warehouse 72

Data Warehouse Cloud Kafka Cloud Storage 72

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.

Cloud 40

Cloud Big Data Metadata Machine Learning 40

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 Cloud Retail 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

AltexSoft

SEPTEMBER 23, 2021

This often involves such operations as data harmonization, mastering, and enrichment with metadata. Data access layer unites all the access points connected to the data hub (transactional application, BI systems, machine learning training software, etc). Enrichment with metadata is another important thing. Stambia data hub.

Architecture 52

Architecture Data Lake Unstructured Data Data Warehouse 52

Rockset

JANUARY 23, 2024

Example application with frequent updates To better understand use cases that have frequent updates , let’s look at a search application for a video streaming service like Netflix. When a user searches for a show, ie “political thriller”, they are returned a set of relevant results based on keywords and other metadata.

Metadata 52

Metadata Coding Analytics Application Python 52

AltexSoft

OCTOBER 21, 2022

The tool takes care of storing metadata about partitions and brokers. Hadoop fits heavy, not time-critical analytics applications that generate insights for long-term planning and strategic decisions. ZooKeeper issue. Besides, it defines which broker will take controlling functions. Kafka vs ETL.

Kafka 93

Kafka Hadoop Big Data ETL Tools 93

Edureka

JULY 3, 2024

This makes the data ready for consumption by BI tools, analytics applications, or other systems. ADF’s integration with Purview automatically captures metadata about data movement and transformations, creating a comprehensive map of data flow across the enterprise.

Pipeline-centric 52

Pipeline-centric Data Lake Database-centric Data Pipeline 52

U-Next

AUGUST 23, 2022

Tableau may be used for: controlling metadata. Coding and customizing reports are helpful, and tableau is a top-notch visualization tool for corporate information and analytics applications. Simply, Tableau improves everyone’s understanding of data. data import, irrespective of volume and regions.

BI 52

BI Amazon Web Services Database Business Intelligence 52

ProjectPro

JANUARY 31, 2023

NameNode is often given a large space to contain metadata for large-scale files. The metadata should come from a single file for optimal space use and economic benefit. The following are the steps to follow in a NameNode recovery process: Launch a new NameNode using the FsImage (the file system metadata replica).

Big Data 52

Big Data Hadoop Relational Database AWS 52

AltexSoft

JULY 18, 2023

It has in-memory computing capabilities to deliver speed, a generalized execution model to support various applications, and Java, Scala, Python, and R APIs. Spark Streaming enhances the core engine of Apache Spark by providing near-real-time processing capabilities, which are essential for developing streaming analytics applications.

Big Data 52

Big Data Data Process Process Hadoop 52

Knowledge Hut

JULY 26, 2023

It is widely utilized for its great scalability, fault tolerance, and quick write performance, making it ideal for large-scale data storage and real-time analytics applications. As a database application, it is critical to simplify the storage, retrieval, and transfer of media assets across various broadcasting platforms.

Database 52

Database NoSQL MongoDB Telecommunication 52

phData: Data Engineering

JULY 18, 2023

CDWs are designed for running large and complex queries across vast amounts of data, making them ideal for centralizing an organization’s analytical data for the purpose of business intelligence and data analytics applications. Allowing data diff analysis and code generation.

Data Warehouse 52

Data Warehouse Pipeline-centric Government Data 52