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. These tables are designed to handle continuous data streams with automatic schema evolution, allowing them to adapt quickly to changing data requirements.

Data Lake 52

Data Lake Metadata Hadoop Data Governance 52

Data Engineering Podcast

AUGUST 27, 2021

Summary The reason that so much time and energy is spent on data integration is because of how our applications are designed. What are some of the edge cases that users need to consider as they are designing and building those connections?

Data Integration 100

Data Integration Architecture Data Warehouse Data Lake 100

Cloudera

SEPTEMBER 15, 2022

The gathering in 2022 marked the sixteenth year for top data and analytics professionals to come to the MIT campus to explore current and future trends. A key area of focus for the symposium this year was the design and deployment of modern data platforms. Are there things they should keep in mind?

Data Lake 79

Data Lake Analytics Application Cloud Storage Architecture 79

Christophe Blefari

MARCH 24, 2023

Backfilling has to be thought at every step of a data pipeline design and development. Hex is a notebook-based analytics application. Cells are at the center of the analytics, they produce outputs than can be used later in other cells on in visualisation. This is a small LinkedIn article, but it's a good reminder.

Lambda Architecture 130

Lambda Architecture Data Pipeline Data SQL 130

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. RI is a global leader in the design and deployment of large-scale, production-level modern data platforms for the world’s largest enterprises.

Data Lake 81

Data Lake Data Pipeline Data Governance Analytics Application 81

Striim

AUGUST 31, 2023

In this article, we will take a look at the benefits and drawbacks of kappa architecture, how Striim makes it easier to use, what infrastructure you need for your kappa architecture, and how you can start designing your own kappa architecture with a free version of Striim’s unified data integration and streaming platform.

Data Integration 69

Data Integration Architecture Amazon Web Services ETL System 69

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. To learn how Rockset architecture enables fast, compute-efficient analytics on real-time data, read more about Rockset Concepts, Design & Architecture.

Kafka 52

Kafka Data Warehouse BI Analytics Application 52

Rockset

MAY 17, 2022

This is the fourth post in a series by Rockset's CTO Dhruba Borthakur on Designing the Next Generation of Data Systems for Real-Time Analytics. The design goal was low latency and scale. Most analytical queries need this ability to join multiple data sources at query time.

SQL 52

SQL NoSQL Hadoop MongoDB 52

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

This is the first post in a series by Rockset's CTO Dhruba Borthakur on Designing the Next Generation of Data Systems for Real-Time Analytics. How Mutability Enables Real-Time Analytics At Facebook, I helped design mutable analytics systems that delivered real-time speed, efficiency and reliability.

Data Analytics 52

Data Analytics Data Warehouse Medical MySQL 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

OCTOBER 4, 2022

Streaming data feeds many real-time analytics applications, from logistics tracking to real-time personalization. Change data capture (CDC) streams from OLTP databases, which may provide sales, demographic or inventory data, are another valuable source of data for real-time analytics use cases.

MySQL 52

MySQL Kafka Aggregated Data Architecture 52

Knowledge Hut

JUNE 30, 2023

The practice of designing, building, and maintaining the infrastructure and systems required to collect, process, store, and deliver data to various organizational stakeholders is known as data engineering. Data engineers are experts who specialize in the design and execution of data systems and infrastructure. Who are Data Engineers?

Data Engineering 52

Data Engineering Data Engineer Engineering Data Warehouse 52

Towards Data Science

APRIL 26, 2023

Photo by László Glatz on Unsplash In this post we will show how to build a simple end-to-end application in the cloud on a serverless infrastructure. The purpose is simple: we want to show that we can develop directly against the cloud while minimizing the cognitive overhead of designing and building infrastructure.

Engineering 70

Engineering Data Lake AWS BI 70

Snowflake

FEBRUARY 7, 2023

Next-gen product analytics is now warehouse-native, an architectural approach that allows for the separation of code and data. In this model, providers of next-gen product analytics maintain code for the analytical application as a connected app, while customers manage the data in their own cloud data platform.

BI 81

BI Building ETL Tools Data Warehouse 81

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.

Portfolio 52

Portfolio Cloud Analytics Application Data Security 52

Knowledge Hut

MARCH 15, 2024

5) AZ-204: Microsoft Azure Developer Associate Developers working on cloud projects in all stages—from requirements, definition, and design through development, deployment, and maintenance to performance tuning and monitoring—are an ideal group for this Professional Certificate.

Cloud Computing 52

Cloud Computing Algorithm Certification SQL 52

Knowledge Hut

JUNE 16, 2023

Data Engineer Data engineers are engineers responsible for creating reliable architecture and interface designs to collect and transform data from various sources. Data Visualization Engineer A data visualization engineer is responsible for designing and developing data visualization systems to help users quickly understand complex data sets.

Data Analytics 52

Data Analytics Business Analyst Business Intelligence Consulting 52

Data Science Blog: Data Engineering

JULY 4, 2022

For fast analytic queries against another size of data, it uses in-memory caching and optimised query execution. It is a parallel processing framework for grouped computers to operate large-scale data analytics applications. This could handle packet and real-time data processing and predictive analysis workloads.

Hadoop 52

Hadoop Big Data Datasets Scala 52

Data Science Blog: Data Engineering

NOVEMBER 15, 2023

An object-centric data model is a big deal because it offers the opportunity for a holistic approach and as a database a single source of truth for Process Mining but also for other types of analytical applications. This is something we can literally objectify!

Architecture 52

Architecture Database-centric Process BI 52

Rockset

FEBRUARY 24, 2023

It has expanded to various industries and applications, including IoT sensor data, financial data, web analytics, gaming behavioral data, and many more use cases. This type of data has become an essential component of real-time analytics applications because reacting to events quickly can have major effects on a business’ revenue.

Kafka 52

Kafka AWS Amazon Web Services Programming Language 52

Rockset

JANUARY 23, 2024

Many search and analytics applications can experience frequent updates, especially when built on CDC data. Partial updates using Painless scripts in Elasticsearch Painless is a scripting language designed for Elasticsearch and can be used for query and aggregation calculations, complex conditionals, data transformations and more.

Metadata 52

Metadata Coding Analytics Application Python 52

Rockset

JULY 6, 2022

This is the fifth post in a series by Rockset's CTO and Co-founder 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! And the same risk of data errors and data downtime also exists.

NoSQL 52

NoSQL SQL Systems PostgreSQL 52

Workfall

JUNE 27, 2023

It serves as a central event hub, allowing different microservices or applications to publish and subscribe to events, enabling loose coupling, scalability, and asynchronous communication. Log Aggregation and Analytics: Kafka’s durable and fault-tolerant design makes it an excellent choice for log aggregation.

Kafka 75

Kafka Architecture AWS Transportation 75

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

Knowledge Hut

MAY 3, 2024

Initially, it was designed to respond to the ABC programming language and later gained popularity as a functional language in a big data world. With RStudio, it makes a killer statistic, plotting, and data analytics application. Steep learning curve. Lack of matured machine learning languages.

Scala 52

Scala Java Python Programming Language 52

Knowledge Hut

MAY 31, 2023

Custom analytics applications for IoT data, including machine learning and predictive analytics. MQTT Designed for low-bandwidth communication between devices and a central broker, MQTT (Message Queueing Telemetry Transport) is amongst widely used IoT tools, lightweight publish-subscribe messaging protocol in IoT development.

Programming Language 52

Programming Language Electronics Java Programming 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. TIBCO Spitfire is included in this post due to its extensive functionality.

Business Intelligence 52

Business Intelligence BI Data Mining Data Analysis 52

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. Data warehouses scale well and are well-suited to BI and advanced analytics use cases.

MongoDB 52

MongoDB NoSQL Data Warehouse BI 52

Knowledge Hut

JULY 26, 2023

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? Database applications are software programs or systems that are designed to organize and efficiently store, handle, and retrieve vast amounts of data.

Database 52

Database NoSQL Telecommunication MongoDB 52

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. Kafka vs ETL.

Kafka 93

Kafka Hadoop ETL Tools Big Data 93

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

Rockset

AUGUST 18, 2020

Modern real-time analytics and applications So what does it look like when real-time analytics is used for serving applications and not just dashboards? 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

Striim

NOVEMBER 8, 2023

The four data mesh principles aim to solve major difficulties that have plagued data and analytics applications for a long time. The data mesh more readily acknowledges the dynamic nature of data and allows for domains to designate the governing structures that are most suitable for their data products.

Architecture 52

Architecture Generalist Government Datasets 52

AltexSoft

JULY 18, 2023

Maintained by the Apache Software Foundation, Apache Spark is an open-source, unified engine designed for large-scale data analytics. The building blocks of Apache Spark Apache Spark comprises a suite of libraries and tools designed for data analysis, machine learning , and graph processing on large-scale data sets.

Big Data 52

Big Data Data Process Process Hadoop 52

Rockset

AUGUST 4, 2021

Operational Analytics Applications such as e-commerce, gaming, and the Internet of things (IoT) commonly require real-time views of what’s happening on a site, in a game, or at a manufacturing plant. Rockset is designed for real-time analytics. If those solutions are insufficient, it may be time to explore alternatives.

Data Ingestion 40

Data Ingestion Cloud Storage Data Warehouse Data Lake 40