Data Engineering Podcast

MARCH 20, 2022

What are some heuristics that you have developed for understanding how to manage data lifecycles in a user-facing analytics application? What are some heuristics that you have developed for understanding how to manage data lifecycles in a user-facing analytics application? data tiering, tail latencies, etc.)

Datasets 100

Datasets Kafka Analytics Application Data Engineer 100

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. They are respectively the home companies of Kafka and Flink. I think this is even relevant to data world. Confluent signed a deal to acquired Immerok.

Data Science 130

Data Science Data BI Kafka 130

Knowledge Hut

JUNE 26, 2023

Stock and Twitter Data Extraction Using Python, Kafka, and Spark Project Overview: The rising and falling of GameStop's stock price and the proliferation of cryptocurrency exchanges have made stocks a topic of widespread attention. Source Code: Stock and Twitter Data Extraction Using Python, Kafka, and Spark 2.

Data Engineer 98

Data Engineer Data Engineering Coding Project 98

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.

Kafka 40

Kafka Database Process SQL 40

Rockset

JUNE 21, 2022

We’re excited to announce that Rockset’s new connector with Snowflake is now available and can increase cost efficiencies for customers building real-time analytics applications. The historical data would be stored in Snowflake and brought into Rockset for analysis using the connector.

Kafka 52

Kafka Data Warehouse BI Analytics Application 52

ProjectPro

AUGUST 9, 2016

Cognizant’s BIGFrame solution uses Hadoop to simplify migration of data and analytics applications to provide mainframe like performance at an economical cost of ownership over data warehouses.

Hadoop 52

Hadoop Insurance Cloud Computing Big 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. A platform such as Apache Kafka/Confluent , Spark or Amazon Kinesis for publishing that stream of event data.

Data Analytics 52

Data Analytics Data Warehouse MySQL Medical 52

Rockset

FEBRUARY 24, 2023

Introduction Let’s get this out of the way at the beginning: understanding effective streaming data architectures is hard, and understanding how to make use of streaming data for analytics is really hard. Kafka or Kinesis ? A few noteworthy points: Self-managed Kafka can be deployed on-premises or in the cloud.

Kafka 52

Kafka AWS Amazon Web Services Programming Language 52

Edureka

AUGUST 23, 2024

Learning and upgrading your skills to use the latest Kinesis suite will help you build real-time analytics applications that are useful across various industries. What is the difference between Amazon Kinesis and Kafka? Unlike Kinesis, which is a fully managed AWS service, Kafka is an open-source platform.

AWS 52

AWS Kafka Amazon Web Services Medical 52

Rockset

OCTOBER 4, 2022

Streaming data feeds many real-time analytics applications, from logistics tracking to real-time personalization. The broad adoption of Apache Kafka has helped make these event streams more accessible. Flink, Kafka and MySQL. Both offer SQL support and are capable of ingesting streaming data from Kafka.

MySQL 52

MySQL Kafka Aggregated Data Architecture 52

Rockset

MAY 3, 2023

To find out, we decided to test the streaming ingestion performance of Rockset’s next generation cloud architecture and compare it to open-source search engine Elasticsearch , a popular sink for Apache Kafka. Streaming data is on the rise with over 80% of Fortune 100 companies using Apache Kafka. Why measure streaming data ingestion?

Data Ingestion 59

Data Ingestion Kafka Database Architecture 59

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.

Hadoop 40

Hadoop Data Lake Big Data Cloud Computing 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. Indexing Efficiency Indexing data is another crucial requirement for real-time analytics applications.

MongoDB 40

MongoDB Data Ingestion Analytics Application Kafka 40

AltexSoft

SEPTEMBER 23, 2021

As the output, the data collected from various sources becomes consistent and readable for the end-point systems like analytics applications. As for the toolset, there are some well-known solutions such as Apache Kafka , a powerful data integration, event-streaming, and data analytics platform. Source: marklogic.com.

Architecture 52

Architecture Data Lake Unstructured Data Data Warehouse 52

Rockset

AUGUST 18, 2020

One example is an application that combines web analytics with customer data and social data to predict end user behavior, churn, LTV, or just serve more timely content.” This means the lines between real-time analytics and real-time analytical applications are now blurring.

Data Lake 40

Data Lake Data Schemas BI Kafka 40

Rockset

DECEMBER 9, 2019

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. Kafka connectors are available within Rockset to consume streams from Kafka in real time.

Data Engineer 40

Data Engineer Data Engineering Engineering Raw Data 40

ProjectPro

MARCH 10, 2016

All the batch processing and analytics workload at LinkedIn is primarily handled by Hadoop. LinkedIn uses Hadoop for development of predictive analytics applications like “Skill Endorsements” and “People You May Know”, ad-hoc analysis by data scientists and for descriptive statistics for operating internal dashboards.

Hadoop 40

Hadoop Big Data Data Analytics Big Data Ecosystem 40

Rockset

DECEMBER 9, 2021

Real-time data streams typically power analytical or data applications whereas batch systems were built to power static dashboards. This fantastic piece about the anatomy of analytical applications defined a data app as an end-user facing application that natively includes large-scale, aggregate analysis of data in its functionality.

Transportation 52

Transportation BI SQL Database 52

Monte Carlo

MAY 14, 2024

It was designed to support high-volume data exchange and compatibility across different system versions, which is essential for streaming architectures such as Apache Kafka.

Data Lake 52

Data Lake Metadata Hadoop Data Governance 52

Towards Data Science

APRIL 3, 2023

From Enormous Data back to Big Data Say you are tasked with building an analytics application that must process around 1 billion events (1,000,000,000) a day. While this might feel far-fetched at first, due to the sheer size of the data, it often helps to step back and think about the intention of the application (what does it do?)

Process 98

Process Data Lake Systems Data Engineering 98

AltexSoft

JULY 18, 2023

Spark Streaming enhances the core engine of Apache Spark by providing near-real-time processing capabilities, which are essential for developing streaming analytics applications. Netflix leverages Spark Streaming and Kafka for near real-time movie recommendations. Framework Programming The Good and the Bad of Node.js

Big Data 52

Big Data Data Process Process Hadoop 52

Rockset

FEBRUARY 25, 2021

However, Elasticsearch has several limitations that make it less suitable when it comes to running more complex analytical queries. Rockset, on the other hand, provides full-featured SQL and an API endpoint interface that allows developers to quickly join across data sources like DynamoDB and Kafka.

SQL 40

SQL Data Pipeline Kafka Database 40

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 Engineer 52

Data Engineer Data Engineering Engineering Data Warehouse 52

Knowledge Hut

MAY 31, 2023

Custom analytics applications for IoT data, including machine learning and predictive analytics. SiteWhere SiteWhere is a multi-tenant, open-source platform that enables the creation, deployment, and maintenance of industrial-level IoT applications. Kinoma Marvell Technology, Inc.,

Programming Language 52

Programming Language Electronics Java Programming 52

Rockset

APRIL 15, 2022

Explosion in Streaming Data Before Kafka, Spark and Flink, streaming came in two flavors: Business Event Processing (BEP) and Complex Event Processing (CEP). Many (Kafka, Spark and Flink) were open source. However, the environment has become much more demanding for three reasons: 1. They were unaffordable for most companies.

Analytics Application 52

Analytics Application Data Warehouse Kafka Database 52

ProjectPro

MAY 31, 2021

A big data project is a data analysis project that uses machine learning algorithms and different data analytics techniques on a large dataset for several purposes, including predictive modeling and other advanced analytics applications. Big Data Project using Hadoop with Source Code for Web Server Log Processing 5.

Big Data 52

Big Data Coding Project Hadoop 52