Snowflake

DECEMBER 19, 2023

“The Snowflake Native App Framework really helps them give their customers the reassurance that their data is not traveling across the internet, and that they’re able to do all of their data processing within their own environment.” One conversation quickly coming to the forefront is first-party data.

Building 82

Building Pipeline-centric Database-centric Digital Media 82

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

DataKitchen

NOVEMBER 5, 2024

The Race For Data Quality In A Medallion Architecture The Medallion architecture pattern is gaining traction among data teams. It is a layered approach to managing and transforming data. By systematically moving data through these layers, the Medallion architecture enhances the data structure in a data lakehouse environment.

Architecture 79

Architecture Raw Data Pipeline-centric Data Ingestion 79

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

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

Knowledge Hut

NOVEMBER 17, 2023

The demand for data-related professions, including data engineering, has indeed been on the rise due to the increasing importance of data-driven decision-making in various industries. Becoming an Azure Data Engineer in this data-centric landscape is a promising career choice.

Data Engineering 52

Data Engineering Data Engineer Engineering Scala 52

Databand.ai

JULY 19, 2023

This capability is particularly useful in complex data landscapes, where data may pass through multiple systems and transformations before reaching its final destination Impact analysis: When changes are made to data sources or data processing systems, it’s critical to understand the potential impact on downstream processes and reports.

Pipeline-centric 52

Pipeline-centric Data Governance Metadata Government 52

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

Ascend.io

SEPTEMBER 14, 2023

Data engineers can find one for almost any need, from data extraction to complex transformations, ensuring that they’re not reinventing the wheel by writing code that’s already been written. PySpark, for instance, optimizes distributed data operations across clusters, ensuring faster data processing.

Data Engineering 52

Data Engineering Data Engineer Python Engineering 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 97

Architecture Building Kafka Database-centric 97

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 Google Cloud Pipeline-centric Media 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

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

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

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 Cloud 52

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 94

Kafka Pipeline-centric Architecture Database-centric 94

Knowledge Hut

FEBRUARY 27, 2023

Data Engineers indulge in the whole data process, from data management to analysis. Engineers work with Data Scientists to help make the most of the data they collect and have deep knowledge of distributed systems and computer science. Who is Data Engineer, and What Do They Do?

Data Engineering 52

Data Engineering Data Engineer Database-centric Pipeline-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

Edureka

JULY 3, 2024

ADF connects to various data sources, including on-premises systems, cloud services, and SaaS applications. It then gathers and relocates information to a centralized hub in the cloud using the Copy Activity within data pipelines. Transform and Enhance the Data: Once centralized, data undergoes transformation and enrichment.

Pipeline-centric 52

Pipeline-centric Data Lake Database-centric Data Pipeline 52

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