Knowledge Hut

JULY 26, 2023

Database applications also help in data-driven decision-making by providing data analysis and reporting tools. In this blog, we will deep dive into database system applications in DBMS, and their components and look at a list of database applications. What are Database Applications? Spatial Database (e.g.-

Database 52

Database NoSQL Telecommunication MongoDB 52

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. Design Detail.

Data Warehouse 69

Data Warehouse Cloud Kafka Cloud Storage 69

ProjectPro

FEBRUARY 16, 2023

If you are still wondering whether or why you need to master SQL for data engineering, read this blog to take a deep dive into the world of SQL for data engineering and how it can take your data engineering skills to the next level. Your SQL skills as a data engineer are crucial for data modeling and analytics tasks.

Data Engineering 52

Data Engineering Data Engineer SQL Engineering 52

Rockset

MARCH 19, 2020

Intro In recent years, Kafka has become synonymous with “streaming,” and with features like Kafka Streams, KSQL, joins, and integrations into sinks like Elasticsearch and Druid, there are more ways than ever to build a real-time analytics application around streaming data in Kafka. Postgres), and maybe even data lake (i.e.

Kafka 40

Kafka Database Process SQL 40

ProjectPro

FEBRUARY 11, 2021

Having that designation means you can build end-to-end machine learning solutions , which is a highly marketable skill set considering the fact that it has been the fastest-growing job title in the world since 2019. But what does it actually take to achieve the designation of a machine learning engineer? Handling exceptions.

Machine Learning 40

Machine Learning Engineering Programming Language Portfolio 40

Cloudera

SEPTEMBER 15, 2022

It was designed as a native object store to provide extreme scale, performance, and reliability to handle multiple analytics workloads using either S3 API or the traditional Hadoop API. Interoperability of the same data for several workloads: multi-protocol access. Bringing files and objects under one roof.

Systems 86

Systems Hadoop Metadata Telecommunication 86

Rockset

MARCH 15, 2021

The Demands of Real-Time Analytics Real-time analytics applications have specific demands (i.e., and your solution will only be able to provide valuable real-time analytics if you are able to meet them. Thus queries can access data in the memory itself and don’t have to wait until it is written to the disk.

MongoDB 40

MongoDB Data Ingestion Analytics Application Kafka 40

phData: Data Engineering

MAY 11, 2023

In the latest previews of Matillion Designer, we can see that source control is seamlessly incorporated. The next-generation Matillion Designer SaaS offering balances accessibility with a very minor learning curve on Git. The hallmarks of this method are modular design and effective organization skills.

Coding 52

Coding Software Engineer Software Engineering Government 52

Rockset

MAY 3, 2023

In this blog, we’ll walk through the benchmark framework, configuration and results. We’ll also delve under the hood of the two databases to better understand why their performance differs when it comes to search and analytics on high-velocity data streams. Rockset achieved up to 4x higher throughput and 2.5x

Data Ingestion 59

Data Ingestion Kafka Database Architecture 59

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. Because metadata is always associated with your data, you can open up self-service access to more diverse users and apps without those apps becoming data silos in cloud.

Cloud 40

Cloud Metadata Big Data AWS 40

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

Cloudera

APRIL 2, 2019

Intel Optane DC persistent memory was designed with ease of adoption in mind and therefore can be configured in two different operating modes : 1. Apache HBase® is one of many analytics applications that benefit from the capabilities of Intel Optane DC persistent memory. memory mode, and 2. app direct mode.

NoSQL 47

NoSQL Google Cloud Hadoop Machine Learning 47

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. Kafka is designed to handle numerous clients from both sides. Large user community.

Kafka 93

Kafka Hadoop ETL Tools Big Data 93

ProjectPro

SEPTEMBER 6, 2021

AWS vs. GCP blog compares the two major cloud platforms to help you choose the best one. Table of Contents AWS vs. GCP - The Cloud Battle AWS vs. Popular instances where GCP is used widely are machine learning analytics, application modernization, security, and business collaboration. Let’s get started!

AWS 52

AWS Amazon Web Services Google Cloud Cloud Storage 52

Cloudera

NOVEMBER 9, 2018

Armed with those personal data, cybercrooks could use them to illegally access a victim’s bank account or create a composite of the individual to commit offline crimes such as burglary, extortion, or fraud. Realizing IoT devices’ weakness, cybercriminals have been developing more malware designed specifically to exploit those devices.

Machine Learning 56

Machine Learning Banking Government Media 56

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. HBase storage is ideal for random read/write operations, whereas HDFS is designed for sequential processes.

Big Data 52

Big Data Hadoop AWS Relational Database 52

ProjectPro

MAY 23, 2015

Get FREE Access to Data Analytics Example Codes for Data Cleaning, Data Munging, and Data Visualization Walmart was the world’s largest retailer in 2014 in terms of revenue. We want to know who every person in the world is. And we want to have the ability to connect them together in a transaction.”

Big Data 52

Big Data Data Analysis Hadoop Retail 52

Cloudera

OCTOBER 12, 2020

We believe Eventador will accelerate innovation in our Cloudera DataFlow streaming platform and deliver more business value to our customers in their real-time analytics applications. Our primary design principles are self-service, simplicity and hybrid. Risk management and real-time fraud analysis for IT and finance teams.

Cloud 131

Cloud Process Scala Kafka 131

ProjectPro

MAY 31, 2021

This blog lists over 20 big data projects you can work on to showcase your big data skills and gain hands-on experience in big data tools and technologies. Here are some options for collecting data that you can utilize: Connect to an existing database that is already public or access your private database.

Big Data 52

Big Data Coding Project Hadoop 52

Rockset

APRIL 15, 2022

This is the second post in a series by Rockset's CTO Dhruba Borthakur on Designing the Next Generation of Data Systems for Real-Time Analytics. We'll be publishing more posts in the series in the near future, so subscribe to our blog so you don't miss them! That is called at-least-once semantics. This has some benefits.

Analytics Application 52

Analytics Application Data Warehouse Raw Data Kafka 52

phData: Data Engineering

JULY 18, 2023

Imagine a world where all of your data is organized, easily accessible, and routinely leveraged to drive impactful outcomes. Central Source of Truth for Analytics A Cloud Data Warehouse (CDW) is a type of database that provides analytical data processing and storage capabilities within a cloud-based infrastructure.

Data Warehouse 52

Data Warehouse Pipeline-centric Government Data 52