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

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

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

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.

Cloud Computing 52

Cloud Computing Certification Pipeline-centric Database-centric 52

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

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? They are required to have deep knowledge of distributed systems and computer science.

Data Engineering 98

Data Engineering Data Engineer Engineering Pipeline-centric 98

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

Snowflake

JUNE 28, 2023

Snowpark is our secure deployment and processing of non-SQL code, consisting of two layers: Familiar Client Side Libraries – Snowpark brings deeply integrated, DataFrame-style programming and OSS compatible APIs to the languages data practitioners like to use. Previously, tasks could be executed as quickly as 1-minute.

Python 52

Python Accessible Accessibility Pipeline-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

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

U-Next

AUGUST 17, 2022

Data Engineers must be proficient in Python to create complicated, scalable algorithms. This language provides a solid basis for big data processing and is effective, flexible, and ideal for text analytics. Data Engineers focused on pipelines require a solid understanding of decentralized technology and computer engineering.

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

Monte Carlo

NOVEMBER 14, 2023

This is the world that data orchestration tools aim to create. Data orchestration tools minimize manual intervention by automating the movement of data within data pipelines. According to one Redditor on r/dataengineering, “Seems like 99/100 data engineering jobs mention Airflow.”

Pipeline-centric 40

Pipeline-centric Google Cloud Data Workflow Python 40

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

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

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

Confluent

JUNE 18, 2019

It involves many moving parts, from data preparation to building indexing and query pipelines. Luckily, this task looks a lot like the way we tackle problems that arise when connecting data. Building an indexing pipeline at scale with Kafka Connect. It is a natural evolution from the initial application-centric setup.

Architecture 96

Architecture Building Kafka Database-centric 96

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

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 93

Kafka Pipeline-centric Architecture Database-centric 93

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

Databand.ai

DECEMBER 29, 2022

Seth champions exponential change by combining existing technologies and data science to create industrial scale processes including innovative automation, IT systems and analysis pipelines to support these.

BI 52

BI Consulting Data Science Data Governance 52

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. He is also a member of The Apache Software Foundation.

Data Analytics 52

Data Analytics Google Cloud Data Science Data Mining 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