Striim

MARCH 21, 2025

Streaming data integration offers Change Data Capture technology. You end up having data flowing in the right format for your cloud analytics solution. The main value from this is that you can now run operational workloads, high value or high operational value producing analytics applications in your analytics solution.

Cloud 52

Cloud Database Architecture BI 52

Striim

MARCH 21, 2025

Streaming data integration offers Change Data Capture technology. You end up having data flowing in the right format for your cloud analytics solution. The main value from this is that you can now run operational workloads, high value or high operational value producing analytics applications in your analytics solution.

Cloud 52

Cloud Database Architecture BI 52

Cloudera

MARCH 2, 2021

But as data streaming technologies like Apache Kafka and Apache Flink have evolved, only until recently have SQL interfaces become deeply integrated. Structured Query Language (SQL) has enjoyed half a century of dominance as the de facto interface to data.

SQL 112

SQL Java Data Lake Scala 112

Christophe Blefari

JANUARY 7, 2023

Introducing ADBC: Database Access for Apache Arrow — When I see "minimal-overhead alternative to JDBC/ODBC for analytical applications" I'm instantly in. This is to be honest a natural move because the two technologies works at the best together and ksqlDB never took the place it should have been in the market.

Data Science 130

Data Science Data BI Kafka 130

Data Engineering Podcast

AUGUST 27, 2021

In this episode Dan DeMers, Cinchy’s CEO, explains how their concept of a "Dataware" platform eliminates the need for costly and error prone integration processes and the benefits that it can provide for transactional and analytical application design. What do you have planned for the future of Cinchy?

Data Integration 100

Data Integration Architecture Data Warehouse Data Lake 100

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.,

Hadoop 94

Hadoop Government Data Security Cloud 94

Cloudera

DECEMBER 1, 2022

For analytic applications to properly leverage a hybrid, multi-cloud ecosystem to support modern data architectures, data observability has become even more important. Mark: It is critical that the technologies selected within the data fabric provide the foundation for capturing and leveraging the insights from data observability.

Data Lake 84

Data Lake Data Pipeline Analytics Application Architecture 84

Snowflake

FEBRUARY 7, 2023

Event data for tracking a user’s journey has always been important to product analytics—but we’re now seeing changes in how businesses work with and manage their data, including event data. Next-gen product analytics is now warehouse-native, an architectural approach that allows for the separation of code and data.

BI 83

BI Building ETL Tools Data Warehouse 83

Cloudera

NOVEMBER 2, 2020

This is resulting in advancements of what is provided by the technology, and a resulting shift in the art of the possible. These will provide more details on how the technologies work together and how you can build your own RTDW applications. Users today are asking ever more from their data warehouse. What’s Next?

Data Warehouse 97

Data Warehouse Kafka Lambda Architecture Telecommunication 97

AltexSoft

OCTOBER 21, 2022

Kafka can continue the list of brand names that became generic terms for the entire type of technology. The technology was written in Java and Scala in LinkedIn to solve the internal problem of managing continuous data flows. Similar to other popular open-source technologies, Kafka has a vast community of users and contributors.

Kafka 93

Kafka Hadoop Big Data ETL Tools 93

ProjectPro

AUGUST 9, 2016

According to premier rating and networking website-LinkedIn, Cognizant Technology Solutions (CTS) is among the most sought after companies to work for in India. According to Glassdoor, Hadoop Developer salaries at Cognizant Technology Solutions can range from $68,240-$98,446.As

Hadoop 52

Hadoop Insurance Cloud Computing Big Data 52

Striim

AUGUST 31, 2023

By eliminating manual processes such as ETL (extract-transform-load) systems, companies can save time and money while still leveraging advanced technologies like machine learning and artificial intelligence (AI). However, this technology is not without its drawbacks.

Data Integration 69

Data Integration Architecture Amazon Web Services Machine Learning 69

Knowledge Hut

JUNE 26, 2023

Before working on these initiatives, you should be conversant with topics and technologies. Top 4 Data Engineering Project Ideas: Beginner & Final Year Students Becoming an expert data engineer necessitates familiarity with the best practices and cutting-edge technologies in your field. Master data processing methods.

Data Engineering 98

Data Engineering Data Engineer Coding Project 98

Rockset

FEBRUARY 18, 2021

Using RockBench, we ascertained Rockset’s suitability for many real-time analytics applications due to its ability to keep data latency to under 1 second, while ingesting 1 billion events per day, on a standard 4XLarge Virtual Instance. The SSB has also been used for performance measurements of other modern data technologies.

Analytics Application 52

Analytics Application SQL Database Structured Data 52

Rockset

MARCH 10, 2022

To deliver real-time analytics, companies need a modern technology infrastructure that includes these three things: A real-time data source such as web clickstreams, IoT events produced by sensors, etc. 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

Workfall

JUNE 27, 2023

It enables real-time data processing, transformation, and analysis by integrating with stream processing frameworks like Apache Spark, Apache Flink, or Kafka Streams, making it a powerful tool for building data pipelines and real-time analytics applications.

Kafka 75

Kafka Architecture AWS Transportation 75

Knowledge Hut

DECEMBER 26, 2023

Business intelligence (BI) is the collective name for a set of processes, systems, and technologies that turn raw data into knowledge that can be used to operate enterprises profitably. Business intelligence solutions comBIne technology and strategy for gathering, analyzing, and interpreting data from internal and external sources.

Business Intelligence 52

Business Intelligence BI Data Mining Data Analysis 52

Precisely

MARCH 10, 2023

With the right geocoding technology, accurate and standardized address data is entirely possible. This capability opens the door to a wide array of data analytics applications. The Rise of Cloud Analytics Data analytics has advanced rapidly over the past decade. That effort led to a dramatic shift toward the cloud.

Datasets 58

Datasets Data Warehouse Big Data Data Analytics 58

Rockset

AUGUST 11, 2020

I was simply stunned that Facebook’s technology had the ‘magic’ to connect me to three people who were my cricket-teammates when I was in elementary school. Facebook’s ‘magic’, then, was powered by the ability to process large amounts of information on a new system called Hadoop and the ability to do batch-analytics on it. Why is that?

Hadoop 52

Hadoop Banking Datasets Analytics Application 52

Rockset

MAY 17, 2022

GraphQL’s main analytics shortcoming is its lack of expressive power to join two disparate datasets based on the value of specific fields in those two datasets. Most analytical queries need this ability to join multiple data sources at query time. For complex analytical queries, SQL is unquestionably the best tool.

SQL 52

SQL NoSQL Hadoop MongoDB 52

ProjectPro

SEPTEMBER 9, 2016

Based on the maturity with big data, HCL helps its clients identify use cases to experiment with big data, create data lakes and deploy hadoop data management platforms to develop analytic applications. As of 18 th August, 2016, Glassdoor listed 9 hadoop job openings in US alone.

Hadoop 40

Hadoop Data Lake Big Data Cloud Computing 40

Snowflake

MAY 4, 2023

The way we’d normally design analytical applications is to build something outside of our data environment, manage the infrastructure and compute, connect to Snowflake for the data, take the data back to the compute platform, and give an interface to our users. Click here for all the details.

Portfolio 52

Portfolio Cloud Analytics Application Data Security 52

ProjectPro

SEPTEMBER 6, 2021

Cloud Technology has risen in the latter half of the past decade. Amazon and Google are the big bulls in cloud technology, and the battle between AWS and GCP has been raging on for a while. The Google trends graph above shows how the two technologies have increased over the years, with AWS maintaining a significant margin over GCP.

AWS 52

AWS Amazon Web Services Google Cloud Cloud Storage 52

Knowledge Hut

JUNE 16, 2023

Let’s look into the exciting world of Data Analytics Careers in this digital age! With technological developments every day, the data analytics career is predicted to be in high demand in the upcoming years. With the help of data analytics, businesses get actionable insight to compete with others.

Data Analytics 52

Data Analytics Business Analyst Consulting Business Intelligence 52

Rockset

OCTOBER 26, 2023

With Rockset’s Converged Indexing technology , data is indexed in a search index, columnar store, ANN index and row store for millisecond-latency analytics across a wide range of query patterns. 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

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 Machine Learning Metadata 40

Cloudera

DECEMBER 7, 2017

Cloud service providers now offer a plethora of “house brand” analytics services, some brand new, some re-purposed versions of older technologies. Yet there is often a dark side to using public cloud service providers’ analytics offerings in the cloud, too. We announced SDX at Strata New York in September 2017.

Building 40

Building Management Government BI 40

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 . The best aspect is that technologies are getting inexpensive.

Insurance 40

Insurance Finance Transportation Machine Learning 40

Knowledge Hut

MARCH 15, 2024

To thrive in the IT industry, one must be able to keep up with the swift advancements in technology. Obtaining a certification is a great way to keep up with the most recent trends and learn about the newest developments in technology. Furthermore, certifications in Microsoft Azure provide excellent job options.

Cloud Computing 52

Cloud Computing Certification Algorithm SQL 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

Knowledge Hut

JUNE 30, 2023

It covers popular technologies such as Apache Kafka, Apache Storm, and Apache Hadoop, giving users practical advice on developing and executing effective data pipelines. With helpful illustrations and thorough explanations, it assists readers in comprehending how to use Spark for big data processing and analytics applications.

Data Engineering 52

Data Engineering Data Engineer Engineering Data Warehouse 52

ProjectPro

MAY 20, 2015

According to NASSCOM, the global big data analytics market is anticipated to reach $121 billion by 2016. Another research report by IDC predicts 27% compound annual growth rate for big data services and technologies by end of 2017 which equals 6 times the CAGR of the IT market as a whole.

Big Data 40

Big Data Hadoop Business Analyst Data Analytics 40

Rockset

FEBRUARY 2, 2022

Let’s explore five ways to run MongoDB analytics, along with the pros and cons of each method. 1 – Query MongoDB Directly The first and most direct approach is to run your analytical queries directly against MongoDB. There are many reasons this isn’t most developers’ favored approach, though.

MongoDB 52

MongoDB NoSQL Data Warehouse BI 52

U-Next

SEPTEMBER 29, 2022

During the forecast period, the global Workforce Analytics Market is expected to grow at a Compound Annual Growth Rate (CAGR) of 15.3% Enterprises are facing the immense challenge of analyzing HR data structure in real-time, driving a rapid increase in the demand for advanced analytical tools and analytics applications. .

IT 52

IT Recruitment Data Analysis Consulting 52

Knowledge Hut

MAY 31, 2023

The IoT is a network of physical objects embedded with technology, such as sensors and software. This platform provides a range of IoT tools and technologies to help developers build and manage IoT systems, including device management, data processing, and analytics. Kinoma Marvell Technology, Inc.,

Programming Language 52

Programming Language Electronics Java Programming 52

AltexSoft

SEPTEMBER 23, 2021

We’ll also analyze some of the data hub products existing in the market to explore how this technology works and what it actually can do. It’s not a single technology, but rather an architectural approach that unites storages, data integration and orchestration tools. What is Data Hub? When do you need a data hub?

Architecture 52

Architecture Data Lake Unstructured Data Data Warehouse 52