AltexSoft

OCTOBER 3, 2022

” Basically, a knowledge graph is obtained in the process of filling ontologies with instances of real data. Due to the fact that every company or even individual creates their own version of knowledge graphs, you won’t find a single standardized definition. People explaining knowledge graphs be like… ?.

Relational Database 52

Relational Database Banking Media Computer Science 52

Ascend.io

MAY 24, 2023

This article is based on a presentation given by Sarwat Fatima , Principal Data Engineer at Biome Analytics, at the Data Pipeline Automation Summit 2023. But as data engineering professionals, we’re well aware that handling this data is no easy task. The answer lies in building efficient healthcare data pipelines.

Healthcare 52

Healthcare Data Pipeline Hospitality Datasets 52

dbt Developer Hub

FEBRUARY 7, 2022

Jaffle Shop is a demo repo referenced in dbt’s Getting Started Guide , and its jaffles hold a special place in the dbt community’s hearts, as well as on Data Twitter™. So, I thought it only apt to build on the collective reverence for these tasty, crunchy snacks to talk about customer 360 views. What's a customer 360? ​

Data Warehouse 52

Data Warehouse Datasets Data SQL 52

Striim

NOVEMBER 8, 2023

With the help of Striim’s enterprise-grade platform, companies can now deploy and manage a data mesh architecture with automated data mapping, cloud-native capabilities, and real-time analytics. Organizations can have data product managers who control the data in their domain.

Architecture 52

Architecture Generalist Government Datasets 52

Monte Carlo

MARCH 2, 2023

Otherwise you may produce more data anomalies than you prevent. Data Contracts Image courtesy of Andrew Jones. You can think of data contracts as circuit breakers, but for data schemas instead of the data itself. These ownership lines need to be traced back to owners of the business processes they support.

Food 52

Food Data SQL Data Pipeline 52

Pinterest Engineering

MAY 2, 2023

However, when we process a new event for a user, we do not want to read the existing N events, update them, and then write them all back to the respective dataset. To build up this system, the cost comes from two parts: computing and storage. We ensure the heavy processing of one use case does not affect others.

Lambda Architecture 66

Lambda Architecture Datasets Software Engineer Software Engineering 66

Cloud Academy

JANUARY 27, 2022

Data engineering is the process of designing and implementing solutions to collect, store, and analyze large amounts of data. This process is generally called “Extract, Transfer, Load” or ETL. The format of the data will be different depending on the intended audience.

Data Engineering 52

Data Engineering Data Engineer Certification Engineering 52

Towards Data Science

FEBRUARY 15, 2023

In this context, data management in an organization is a key point for the success of its projects involving data. One of the main aspects of correct data management is the definition of a data architecture. Spark: The definitive guide: Big data processing made simple. O’Reilly Media, Inc.” [2]

Data Lake 78

Data Lake Data Warehouse Hadoop Data Architecture 78

Zalando Engineering

SEPTEMBER 28, 2022

In this post, George and Daniel describe the product that was built to serve this purpose - its problem space, the solution design process, the technological context and how the product evolved to include new use-cases, such as the Zalando Sustainability topic. Our goal was to streamline this into an efficient, user-friendly process.

Certification 52

Certification Architecture Data Schemas Metadata 52

Monte Carlo

JANUARY 25, 2023

Batch in the warehouse Data warehouses tend to operate in a batch environment rather than using stream processing like we do when moving data from production services. One of the bigger differences between these two types of systems comes in how we think about processing data. Image courtesy of Chad Sanderson.

Data Warehouse 52

Data Warehouse Data High Quality Data Metadata 52

Rockset

OCTOBER 6, 2022

Consequently, we needed a data backend with the following characteristics: Scale With ~50 commits per working day (and thus at least 50 pull request updates per day) and each commit running over one million tests, you can imagine the storage/computation required to upload and process all our data.

AWS 52

AWS Data Schemas Accessible Accessibility 52

Netflix Tech

DECEMBER 14, 2018

A schemaless system appears less imposing for application developers that are producing the data, as it (a) spares them from the burden of planning and future-proofing the structure of their data and, (b) enables them to evolve data formats with ease and to their liking. This is depicted in Figure 1.

Media 94

Media Database Metadata Data Schemas 94

Confluent

MAY 28, 2019

If that happens and schema IDs are no longer globally unique, there may be collisions between schema IDs. For consistency in schema definitions and operational simplicity, deploy a single global Schema Registry cluster across an entire company, geographical areas, or clusters in a multi-datacenter design.

Management 82

Management Kafka Java Certification 82

ProjectPro

APRIL 26, 2016

Pig vs Hive Criteria Pig Hive Type of Data Apache Pig is usually used for semi structured data. Used for Structured Data Schema Schema is optional. Hive requires a well-defined Schema. Language It is a procedural data flow language. Partitions are created when data is inserted into the table.

Hadoop 40

Hadoop Metadata SQL Database 40

ProjectPro

AUGUST 21, 2015

Hadoop vs RDBMS Criteria Hadoop RDBMS Datatypes Processes semi-structured and unstructured data. Processes structured data. Schema Schema on Read Schema on Write Best Fit for Applications Data discovery and Massive Storage/Processing of Unstructured data.

Hadoop 40

Hadoop Big Data Metadata Java 40