Christophe Blefari

JANUARY 7, 2023

The blog crossed the 2000 members mark (❤️) and I won the best data science newsletter award. Introducing ADBC: Database Access for Apache Arrow — When I see "minimal-overhead alternative to JDBC/ODBC for analytical applications" I'm instantly in.

Data Science 130

Data Science Data BI Kafka 130

Cloudera

SEPTEMBER 15, 2022

Modern data platforms deliver an elastic, flexible, and cost-effective environment for analytic applications by leveraging a hybrid, multi-cloud architecture to support data fabric, data mesh, data lakehouse and, most recently, data observability. The post Demystifying Modern Data Platforms appeared first on Cloudera Blog.

Data Lake 83

Data Lake Analytics Application Cloud Storage Architecture 83

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 2, 2020

Optimized access to both full fidelity raw data and aggregations. Optimized access to both current data and historical data. Time Series and Event Analytics Specialized RTDW. Analytics storage engine for huge volumes of fast arriving data. Mutability, random access, fast scans, interactive queries.

Data Warehouse 97

Data Warehouse Kafka Lambda Architecture Telecommunication 97

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? Conclusion.

Kafka 88

Kafka Manufacturing Data Lake SQL 88

Cloudera

NOVEMBER 22, 2021

SDX , which is an integral part of CDP , delivers uniform data security and governance, coupled with data visualization capabilities enabling quick onboarding of data and data platform consumers and access to insights for all of CDP across hybrid clouds at no extra cost. benchmarking study conducted by independent 3rd party ). Conclusion .

Hadoop 94

Hadoop Government Data Security Cloud 94

Rockset

FEBRUARY 18, 2021

times faster than Druid in the latest performance blog post. Real-time analytics is all about deriving insights and taking actions as soon as data is produced. When broken down into its core requirements, real-time analytics means two things: access to fresh data and fast responses to queries. Learn how Rockset is 1.67

Analytics Application 52

Analytics Application SQL Database Structured Data 52

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 84

Metadata Algorithm Hadoop Cloud 84

Rockset

OCTOBER 26, 2023

That’s why JetBlue innovates with real-time analytics and AI, using over 15 machine learning applications in production today for dynamic pricing, customer personalization, alerting applications, chatbots and more. Rockset provides the speed and scale required of ML applications accessed daily by over 2,000 employees at JetBlue.

Machine Learning 64

Machine Learning Data Science Architecture Database 64

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 Analytics Application 40

Rockset

MAY 17, 2022

We'll be publishing more posts in the series in the near future, so subscribe to our blog so you don't miss them! More application code not only takes more time to create, but it almost always results in slower queries.

SQL 52

SQL NoSQL Hadoop MongoDB 52

Rockset

JANUARY 30, 2024

In 2023, Rockset announced a new cloud architecture for search and analytics that separates compute-storage and compute-compute. With this architecture, users can separate ingestion compute from query compute, all while accessing the same real-time data. This is a game changer in disaggregated, real-time architectures.

Data Ingestion 64

Data Ingestion Utilities Architecture SQL 64

Rockset

MARCH 10, 2022

We'll be publishing more posts in the series in the near future, so subscribe to our blog so you don't miss them! Analytical queries could be accelerated by caching heavily-accessed read-only data in RAM or SSDs. Get faster analytics on fresher data, at lower costs, by exploiting indexing over brute-force scanning.

Data Analytics 52

Data Analytics Data Warehouse MySQL Medical 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 72

Data Warehouse Cloud Kafka Cloud Storage 72

Snowflake

MAY 4, 2023

In the end, we want all of DTCC’s data securely accessible to our internal and external stakeholders. Forward-Looking Statements This blog contains express and implied forward-looking statements, including statements regarding Snowflake and DTCC’s products, services, and technology offerings that are under development.

Portfolio 52

Portfolio Cloud Analytics Application Data Security 52

Cloudera

APRIL 2, 2019

Apache HBase® is one of many analytics applications that benefit from the capabilities of Intel Optane DC persistent memory. HBase is a distributed, scalable NoSQL database that enterprises use to power applications that need random, real time read/write access to semi-structured data.

NoSQL 48

NoSQL Google Cloud Hadoop Machine Learning 48

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 post The Future of Cloud-based Analytics (Part 3) appeared first on Cloudera Blog.

Cloud 40

Cloud Big Data Metadata Machine Learning 40

Cloudera

DECEMBER 7, 2017

Now we are releasing the reference architecture for you build your own self-managed SDX foundation for all your cloud-based data and analytics applications. Cloud data that can efficiently drive all your machine learning and analytics initiatives. It’s time for a new approach to analytics in the cloud. It’s time for SDX.

Building 40

Building Management Government BI 40

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

Edureka

AUGUST 23, 2024

The AWS training will prepare you to become a master of the cloud, storing, processing, and developing applications for the cloud data. This blog will explore the AWS Amazon Kinesis and how this managed platform can revamp data analytics. This supplies data to the applications waiting to use it. How Amazon Kinesis Works?

AWS 52

AWS Kafka Amazon Web Services Medical 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

Rockset

DECEMBER 9, 2019

Rockset is a real time analytics engine that allows SQL queries directly on raw data, such as nested JSON and XML. It continuously ingests raw data from multiple sources--data lakes, data streams, databases--into its storage layer and allows fast SQL access from both visualisation tools and analytic applications.

Data Engineering 40

Data Engineering Data Engineer Engineering Raw Data 40

Striim

NOVEMBER 8, 2023

Data Mesh is a revolutionary event streaming architecture that helps organizations quickly and easily integrate real-time data, stream analytics, and more. It enables data to be accessed, transferred, and used in various ways such as creating dashboards or running analytics.

Architecture 52

Architecture Generalist Government Datasets 52

Cloudera

MAY 16, 2018

Not moving data mitigates data loss, ensuring data integrity and if the platform security of the data lake is inherited, then the data will only be viewed by those with proper access. The post Cross-Functional Trade Surveillance appeared first on Cloudera Blog. Conclusion.

Data Lake 42

Data Lake Electronics Media Unstructured Data 42

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

AltexSoft

OCTOBER 21, 2022

Another security measure is an audit log to track access. Hadoop fits heavy, not time-critical analytics applications that generate insights for long-term planning and strategic decisions. If you are interested in web development, take a look at our blog post on. Large user community. Kafka vs ETL. Web App Development.

Kafka 93

Kafka Hadoop Big Data ETL Tools 93

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

U-Next

SEPTEMBER 21, 2022

And while employing it is a fairly new technology, it already has a wide range of applications. This blog will look at the best contemporary applications of Artificial Intelligence in business. . Applications of AI in Business Operations . That’s all for applications of AI in business. Conclusion .

Insurance 40

Insurance Finance Transportation Machine Learning 40

Rockset

FEBRUARY 25, 2021

Often this lack of structure forces developers to spend a lot of their time engineering ETL and data pipelines so that analysts can access the complex datasets. Joins, in particular, are rarely well supported by alternative real-time analytics solutions. Instead, this data is often semi-structured in JSON or arrays.

SQL 40

SQL Data Pipeline Kafka Database 40

phData: Data Engineering

MAY 11, 2023

The next-generation Matillion Designer SaaS offering balances accessibility with a very minor learning curve on Git. ZDLC is a time-honored practice among data professionals who have grown their careers with the productivity tools available to most business users, such as Microsoft Excel and Access. When Is ZDLC Better Than SDLC?

Coding 52

Coding Software Engineer Software Engineering Project 52

ProjectPro

MAY 20, 2015

There are several big data and business analytics companies that offer a novel kind of big data innovation through unprecedented personalization and efficiency at scale. Which big data analytic companies are believed to have the biggest potential?

Big Data 40

Big Data Hadoop Business Analyst Data Analytics 40

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

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. The Hadoop MapReduce architecture has a Distributed Cache feature that allows applications to cache files. Data Size HDFS stores and processes big data.

Big Data 52

Big Data Hadoop Relational Database AWS 52

ProjectPro

FEBRUARY 11, 2021

Implement statistical analysis and machine learning into highly available and high performance production level systems to provide ease of access to users. Translate the machine learning models defined by data scientists from environments like Python and R notebooks to analytic applications. Handling exceptions.

Machine Learning 40

Machine Learning Engineering Programming Language Portfolio 40

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

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. Demands on the cloud data warehouse are also evolving to require it to become more of an all-in-one platform for an organization’s analytics needs. Read more here.

Data Warehouse 52

Data Warehouse Pipeline-centric Government Data 52