Grouparoo

OCTOBER 6, 2021

This provided a nice overview of the breadth of topics that are relevant to data engineering including data warehouses/lakes, pipelines, metadata, security, compliance, quality, and working with other teams. Open question: how to seed data in a staging environment? Test system with A/A test. Be adaptable.

Data Engineering 52

Data Engineering Data Engineer Engineering Pipeline-centric 52

Knowledge Hut

JULY 3, 2023

Real-time Data ingestion performs the utilization of data from various origins, does the data cleaning, validation, and preprocessing operations and at the end store it in the required format, either structured or unstructured. As real-time insights gain popularity, real-time data ingestion remains vital for companies worldwide.

Data Ingestion 52

Data Ingestion Pipeline-centric Google Cloud Media 52

Maxime Beauchemin

JANUARY 20, 2017

The fact that ETL tools evolved to expose graphical interfaces seems like a detour in the history of data processing, and would certainly make for an interesting blog post of its own. Sure, there’s a need to abstract the complexity of data processing, computation and storage.

Data Engineering 130

Data Engineering Data Engineer Engineering ETL Tools 130

Knowledge Hut

JUNE 26, 2023

Application Management Application management expertise is crucial in an Azure-centric ecosystem. Experience with Azure Kubernetes Service (AKS), Azure Container Instances (ACI), & Azure DevOps pipelines can help achieve this skill. Azure Solution Developer : Solution developers create & deploy applications on Azure.

Cloud Computing 52

Cloud Computing Certification Pipeline-centric Database-centric 52

Knowledge Hut

DECEMBER 26, 2023

Data Engineering is typically a software engineering role that focuses deeply on data – namely, data workflows, data pipelines, and the ETL (Extract, Transform, Load) process. What is the role of a Data Engineer? Let us now understand the basic responsibilities of a Data engineer.

Data Engineering 98

Data Engineering Data Engineer Engineering Pipeline-centric 98

AltexSoft

JULY 18, 2023

Its flexibility allows it to operate on single-node machines and large clusters, serving as a multi-language platform for executing data engineering , data science , and machine learning tasks. Before diving into the world of Spark, we suggest you get acquainted with data engineering in general. Big data processing.

Big Data 52

Big Data Data Process Process Hadoop 52

Ripple Engineering

JANUARY 29, 2024

For Ripple's product capabilities, the Payments team of Ripple, for example, ingests millions of transactional records into databases and performs analytics to generate invoices, reports, and other related payment operations.    A lack of a centralized system makes building a single source of high-quality data difficult.

Database-centric 52

Database-centric Pipeline-centric NoSQL High Quality Data 52

Knowledge Hut

SEPTEMBER 26, 2023

Organisations are constantly looking for robust and effective platforms to manage and derive value from their data in the constantly changing landscape of data analytics and processing. These platforms provide strong capabilities for data processing, storage, and analytics, enabling companies to fully use their data assets.

Data Lake 52

Data Lake Database-centric Pipeline-centric Machine Learning 52

U-Next

AUGUST 17, 2022

When organizing vast amounts of data, Data Engineering skills are most important. Data must be comprehensive and cohesive, and Data Engineers are best at this task with their set of skills. Skills Required To Be A Data Engineer. Data Engineers must be proficient in Python to create complicated, scalable algorithms.

Data Engineering 52

Data Engineering Data Engineer Pipeline-centric Database-centric 52

Ascend.io

JUNE 8, 2023

Treating data as a product is more than a concept; it’s a paradigm shift that can significantly elevate the value that business intelligence and data-centric decision-making have on the business. Data pipelines Data integrity Data lineage Data stewardship Data catalog Data product costing Let’s review each one in detail.

Pipeline-centric 52

Pipeline-centric Database-centric Data Ingestion Data Pipeline 52

Dataquest

JANUARY 25, 2017

A data scientist is only as good as the data they have access to. Most companies store their data in variety of formats across databases and text files. This is where data engineers come in — they build pipelines that transform that data into formats that data scientists can use. Ride database.

Data Engineering 52

Data Engineering Data Engineer Pipeline-centric Database-centric 52

Ascend.io

AUGUST 29, 2023

A star-studded baseball team is analogous to an optimized “end-to-end data pipeline” — both require strategy, precision, and skill to achieve success. Just as every play and position in baseball is key to a win, each component of a data pipeline is integral to effective data management.

Data Pipeline 52

Data Pipeline Pipeline-centric Database-centric Data Ingestion 52

Confluent

JUNE 18, 2019

As the databases professor at my university used to say, it depends. Using SQL to run your search might be enough for your use case, but as your project requirements grow and more advanced features are needed—for example, enabling synonyms, multilingual search, or even machine learning—your relational database might not be enough.

Architecture 95

Architecture Building Kafka Database-centric 95

Ascend.io

MAY 31, 2023

In the modern world of data engineering, two concepts often find themselves in a semantic tug-of-war: data pipeline and ETL. Fast forward to the present day, and we now have data pipelines. Data Ingestion Data ingestion is the first step of both ETL and data pipelines.

Data Pipeline 52

Data Pipeline ETL Tools Pipeline-centric Data Warehouse 52

AltexSoft

JUNE 7, 2021

Hadoop and Spark are the two most popular platforms for Big Data processing. They both enable you to deal with huge collections of data no matter its format — from Excel tables to user feedback on websites to images and video files. Obviously, Big Data processing involves hundreds of computing units.

Big Data Tools 98

Big Data Tools Hadoop Big Data Database-centric 98

Confluent

SEPTEMBER 25, 2019

Of course, this is not to imply that companies will become only software (there are still plenty of people in even the most software-centric companies), just that the full scope of the business is captured in an integrated software defined process. Here, the bank loan business division has essentially become software.

Database-centric 56

Database-centric Kafka Pipeline-centric Retail 56

Confluent

MAY 9, 2019

Storing events in a stream and connecting streams via stream processors provide a generic, data-centric, distributed application runtime that you can use to build ETL, event streaming applications, applications for recording metrics and anything else that has a real-time data requirement. How quickly are payments processed?

Kafka 92

Kafka Pipeline-centric Architecture Database-centric 92

phData: Data Engineering

JULY 18, 2023

Slow Response to New Information: Legacy data systems often lack the computation power necessary to run efficiently and can be cost-inefficient to scale. This typically results in long-running ETL pipelines that cause decisions to be made on stale or old data.

Data Warehouse 52

Data Warehouse Pipeline-centric Government Data 52