Snowflake

MAY 4, 2023

This year, DTCC celebrates its 50th anniversary, and over the past five decades we’ve led the financial services industry through unprecedented historical events and market disruptions. As a result, you should not rely on any forward-looking statements as predictions of future events.

Portfolio 52

Portfolio Cloud Analytics Application Data Security 52

Rockset

JULY 6, 2022

Most notable are real-time event streams. Second, as business conditions change, companies continually need to analyze new data sources, run different types of analytics – or simply update their data types or labels. After debuting Project Nectar, we presented it to a new set of application developers.

NoSQL 52

NoSQL SQL Systems PostgreSQL 52

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 67

Cloud Storage Unstructured Data AWS Analytics Application 67

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. Rockset was 9.4x

Analytics Application 52

Analytics Application SQL Database Structured Data 52

Workfall

JUNE 27, 2023

In this blog, we’ll explore how to harness the power of Kafka to streamline event streaming within a microservices architecture and unlock its full potential for building scalable and responsive systems. We want to always have unified data which is broadcasted to all the client applications simultaneously without any latency.

Kafka 75

Kafka Architecture AWS Transportation 75

Rockset

MAY 17, 2022

The truth is that modern cloud native SQL databases support all of the key features necessary for real-time analytics , including: Mutable data for incredibly fast data ingestion and smooth handling of late-arriving events. Flexible schemas that can adjust automatically based on the structure of the incoming streaming data.

SQL 52

SQL NoSQL Hadoop MongoDB 52

Knowledge Hut

JUNE 26, 2023

Pipeline for Real-Time Data Processing in Music Applications Project Overview: The project will stream events that are created by a fictitious music streaming service that operates similarly to Spotify. You will be able to generate a sample dataset for this project by using Eventism and the Million Songs dataset.

Data Engineering 98

Data Engineering Data Engineer Coding Project 98

Rockset

AUGUST 18, 2020

Gartner defines Continuous intelligence as a design pattern in which real-time analytics are integrated into business operations, processing current and historical data to prescribe actions in response to business moments and other events.

Data Lake 40

Data Lake Data Schemas BI Kafka 40

Knowledge Hut

MAY 31, 2023

Custom analytics applications for IoT data, including machine learning and predictive analytics. It allows to define custom rules and actions based on events or conditions REST API for remote control and monitoring Customizable user interface can be accessed from web browsers or mobile devices.

Programming Language 52

Programming Language Electronics Java Programming 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. Low Maintenance.

Data Warehouse 69

Data Warehouse Cloud Kafka Cloud Storage 69

Data Engineering Podcast

MARCH 3, 2024

Join us at the top event for the global data community, Data Council Austin. Don't miss out on our only event this year! What are the key considerations for powering AI applications that are substantially different from analytical applications? Don't miss out on our only event this year! Data Council :

Programming 162

Programming Data Lake High Quality Data Data Pipeline 162

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. To accomplish this we can use Kafka streams and KSQL.

Kafka 40

Kafka Database Process SQL 40

Rockset

OCTOBER 4, 2022

Streaming data feeds many real-time analytics applications, from logistics tracking to real-time personalization. Event streams, such as clickstreams, IoT data and other time series data, are common sources of data into these apps. The broad adoption of Apache Kafka has helped make these event streams more accessible.

MySQL 52

MySQL Kafka Aggregated Data Architecture 52

Data Science Blog: Data Engineering

NOVEMBER 15, 2023

New big data architectures and, above all, data sharing concepts such as Data Mesh are ideal for creating a common database for many data products and applications. The Event Log Data Model for Process Mining Process Mining as an analytical system can very well be imagined as an iceberg.

Architecture 52

Architecture Database-centric Process BI 52

ProjectPro

MARCH 10, 2016

LinkedIn processes thousands of events every day. All the batch processing and analytics workload at LinkedIn is primarily handled by Hadoop. Kafka at LinkedIn is used for tracking hundreds of different events like page views, profile views, network updates, impressions, logins and searches over a billion records every day.

Hadoop 40

Hadoop Big Data Data Analytics Big Data Ecosystem 40

Cloudera

AUGUST 13, 2021

The business is so unprepared with information overflow that missing an important event or insight has become the business norm. The application license and infrastructure costs are rising faster than the ability to detect cyber events effectively. What product can help collect events only?

Data Pipeline 108

Data Pipeline Data Lake ETL Tools Unstructured Data 108

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. The first generation of product analytics tooling was primarily about tracking and managing front-end events.

BI 81

BI Building ETL Tools Data Warehouse 81

Rockset

FEBRUARY 24, 2023

Typically, the “units” of data being streamed are considered events, which resemble a record in a database, with some key differences. First, event data is unstructured or semi-structured and stored in a nested format like JSON or AVRO. Second, events are usually immutable (this will be a very important feature in this series!).

Kafka 52

Kafka AWS Amazon Web Services Programming Language 52

Knowledge Hut

DECEMBER 26, 2023

Given its status as one of the complete all-in-one analytics and BI systems available currently, the platform requires some getting accustomed to. Some key features include business intelligence, enterprise planning, and analytics application. You can discover your insights by posing and addressing your questions. Join us now!

Business Intelligence 52

Business Intelligence BI Data Mining Data Analysis 52

Cloudera

NOVEMBER 2, 2020

Ingest 100s of TB of network event data per day . The capabilities that more and more customers are asking for are: Analytics on live data AND recent data AND historical data. The capabilities that more and more customers are asking for are: Analytics on live data AND recent data AND historical data. 200,000 queries per day.

Data Warehouse 91

Data Warehouse Kafka Lambda Architecture Telecommunication 91

AltexSoft

OCTOBER 21, 2022

This scenario involves three main characters — publishers, subscribers, and a message or event broker. A publisher (say, telematics or Internet of Medical Things system) produces data units, also called events or messages , and directs them not to consumers but to a middleware platform — a broker. Kafka cluster and brokers.

Kafka 93

Kafka Hadoop ETL Tools Big Data 93

Rockset

MAY 3, 2023

Many industries including gaming, internet and financial services are mature in their adoption of event streaming platforms and have already graduated from data streams to torrents. The data generator generates documents, each document is the size of 1.25KB and represents a single event.

Data Ingestion 59

Data Ingestion Kafka Database Architecture 59

Precisely

MARCH 10, 2023

By zeroing in on location, you can better understand relationships between people, structures, and events in the physical world. 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.

Datasets 59

Datasets Data Warehouse Big Data Cloud Storage 59

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

Cloudera

MAY 16, 2018

This approach also relates to monitoring internal fiduciary risk by tying separate events together, such as a large position (relative to historic norms) being taken immediately after the risk model that would have flagged it was modified in a separate system.

Data Lake 41

Data Lake Electronics Media Unstructured Data 41

Cloudera

APRIL 2, 2019

When in memory mode, the data is not saved in the event of a power loss. In app direct mode, the application can utilize DRAM for operations that require low latency without persistency and leave Intel Optane DC persistent memory to handle large data structures at memory bus speeds without volatility.

NoSQL 47

NoSQL Google Cloud Hadoop Machine Learning 47

ProjectPro

MAY 23, 2015

For example, an application can be written to subscribe to the Twitter firehose of every tweet written; such an application can analyse the tweets to determine Twitter's most influential users, or identify suddenly prominent events as they occur.

Big Data 52

Big Data Data Analysis Hadoop Retail 52

Rockset

FEBRUARY 25, 2021

Joins are often used in real-time analytics applications to combine streaming data (usually representing events) with static data (like customer information). Because Rockset implemented SQL as its native query language, join functionality was included from day one and not as an afterthought.

SQL 40

SQL Data Pipeline Kafka Database 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.

Architecture 52

Architecture Data Lake Unstructured Data Data Warehouse 52

U-Next

AUGUST 26, 2022

Predictive Analytics. Predictive Analytics indicates the likelihood of an event. It is a useful tool for predicting since it leverages descriptive and analytical analytics outcomes to locate groups and anomalies and predict future trends.

Data Analytics 40

Data Analytics Insurance Transportation Healthcare 40

U-Next

SEPTEMBER 9, 2022

In other words, increasingly, organizations are now using Business Analytics to enable proactive decision-making instead of responding to events as they happen. . . Business Analytics is a flourishing technology as it can be used in every sector where data is accessible and collected.

Retail 40

Retail Big Data Data Analytics Utilities 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.

Metadata 80

Metadata Algorithm Hadoop Cloud 80

Cloudera

JUNE 16, 2022

Apache Flink is a distributed processing engine for stateful computations ideally suited for real-time, event-driven applications. Building real-time data analytics pipelines is a complex problem, and we saw customers struggle using processing frameworks such as Apache Storm, Spark Streaming, and Kafka Streams. .

Kafka 84

Kafka Manufacturing Data Lake SQL 84

Rockset

APRIL 15, 2022

So why are their analytics still crawling through in batches instead of real time? It’s probably because their analytics database lacks the features necessary to deliver data-driven decisions accurately in real time. In other words, events that are close in time are written to adjacent disk clusters or partitions.

Analytics Application 52

Analytics Application Data Warehouse Raw Data Kafka 52

Rockset

DECEMBER 9, 2021

Out-of-order event streams. The batch stack is not built to handle this peculiarity of event streams. Real-time data streams typically power analytical or data applications whereas batch systems were built to power static dashboards. The best event streaming solutions are cloud-based, easy to manage, and cost-effective.

Transportation 52

Transportation BI SQL Data Warehouse 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 52

Big Data Coding Project Hadoop 52