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

U-Next

AUGUST 17, 2022

Companies and enterprises, large and small, are built on data. Data Engineer roles and responsibilities include aiding in the collection of issues and the delivery of remedies addressing customer demand and product accessibility. Data Engineers must be proficient in Python to create complicated, scalable algorithms.

Data Engineering 52

Data Engineering Data Engineer Pipeline-centric Database-centric 52

Knowledge Hut

NOVEMBER 2, 2023

An Azure Data Engineer is a professional responsible for designing, implementing, and managing data solutions using Microsoft's Azure cloud platform. They work with various Azure services and tools to build scalable, efficient, and reliable data pipelines, data storage solutions, and data processing systems.

Data Engineering 52

Data Engineering Data Engineer Engineering Pipeline-centric 52

Ripple Engineering

JANUARY 29, 2024

The key aspect of any business-centric team in delivering products and features is to make critical decisions on ensuring low latency, high throughput, cost-effective storage, and highly efficient infrastructure. Multiple data processing systems also make building detailed dashboards and monitoring very difficult.

Database-centric 52

Database-centric Pipeline-centric NoSQL High Quality Data 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. The spectrum of sources from which data is collected for the study in Data Science is broad.

Data Engineering 98

Data Engineering Data Engineer Engineering Pipeline-centric 98

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

FEBRUARY 27, 2023

A data engineer is a key member of an enterprise data analytics team and is responsible for handling, leading, optimizing, evaluating, and monitoring the acquisition, storage, and distribution of data across the enterprise. Data Engineers indulge in the whole data process, from data management to analysis.

Data Engineering 52

Data Engineering Data Engineer Database-centric Pipeline-centric 52

Data Engineering Weekly

JANUARY 8, 2023

Data Engineering Weekly Is Brought to You by RudderStack RudderStack provides data pipelines that make it easy to collect data from every application, website, and SaaS platform, then activate it in your warehouse and business tools. Pipelines for data in motion can quickly turn into DAG hell.

Data Engineering 40

Data Engineering Data Engineer Engineering Pipeline-centric 40

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

Dataquest

JANUARY 25, 2017

But what about data engineers? 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.

Data Engineering 52

Data Engineering Data Engineer Pipeline-centric Database-centric 52

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

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. scalability.

Big Data Tools 98

Big Data Tools Hadoop Big Data Database-centric 98

Ascend.io

SEPTEMBER 14, 2023

We’ll explore its advantages, delve into its applications, and highlight why Python is increasingly becoming the first choice for data engineers worldwide. Why Python for Data Engineering? As the field of data engineering evolves, the need for a versatile, performant, and easily accessible language becomes paramount.

Data Engineering 52

Data Engineering Data Engineer Python Engineering 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

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. Having this knowledge & expertise is fundamental in today's cloud computing landscape.

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

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. The first application brings with it critical data streams.

Database-centric 56

Database-centric Kafka Pipeline-centric Retail 56

Databand.ai

DECEMBER 29, 2022

She thrives on mentorship, solving data puzzles, and equipping colleagues with new technical skills and is passionate about helping women and non-binary people find their place in the technology industry. Jessica is also active on LinkedIn, posting frequently about data engineering, AI strategy and development, data strategy, and Snowflake.

BI 52

BI Consulting Data Science Data Governance 52

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

The Ultimate Modern Data Stack Migration Guide phData Marketing July 18, 2023 This guide was co-written by a team of data experts, including Dakota Kelley, Ahmad Aburia, Sam Hall, and Sunny Yan. Imagine a world where all of your data is organized, easily accessible, and routinely leveraged to drive impactful outcomes.

Data Warehouse 52

Data Warehouse Pipeline-centric Government Data 52

Edureka

JULY 4, 2024

Databricks runs on an optimized Spark version and gives you the option to select GPU-enabled clusters, making it more suitable for complex data processing. Synapse, with granular access control and easy integration with other Microsoft tools like Azure Active Directory and Power BI, is better for designing the serving layer.

Data Lake 40

Data Lake Pipeline-centric Data Warehouse ETL Tools 40

Databand.ai

DECEMBER 20, 2022

Follow Eric on LinkedIn 10) Brian Femiano Senior Data Engineer at Apple Brian is a senior data engineer with nearly two decades of experience at companies like Booz Allen Hamilton, Magnetic, Pandora, and, most recently, Apple. Wannes is skilled in R, Python, data analysis, and data mining and regularly writes and speaks at events.

Data Analytics 52

Data Analytics Google Cloud Data Science Data Mining 52