Knowledge Hut

SEPTEMBER 25, 2023

This demonstrates how in-demand Microsoft Certified Data Engineers are becoming. They are moving their servers and on-premises data to Azure Cloud. What does all of this mean for Data Engineering professionals? Who is an Azure Data Engineer? Azure Data Engineers work with these and other solutions.

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Knowledge Hut

DECEMBER 28, 2023

Its comprehensive suite of services can handle data at scale. It’s no surprise that the demand for certified Azure data engineers has skyrocketed. Today, Azure Data Engineer certification is an invaluable asset for those looking to excel in the field of data engineering. Who is an Azure Data Engineer?

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Ascend.io

JUNE 10, 2024

Snowflake Summit 2024 has set the stage for exciting changes in the data landscape. As a data enthusiast and a leader in data engineering, I’m eager to share my reflections on these innovations and their implications for Ascend. Snowflake’s investment in expanding data engineering capabilities is a game-changer.

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Knowledge Hut

NOVEMBER 2, 2023

Azure Data engineering projects are complicated and require careful planning and effective team participation for a successful completion. While many technologies are available to help data engineers streamline their workflows and guarantee that each aspect meets its objectives, ensuring that everything works properly takes time.

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phData: Data Engineering

SEPTEMBER 29, 2023

When you think of data engineering , what comes to mind? In reality, though, if you use data (read: any information), you are most likely practicing some form of data engineering every single day. Said differently, any tools or steps we use to help us utilize data can be considered data engineering.

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Knowledge Hut

MARCH 28, 2024

Data engineering is one of them. According to AnalytixLabs , the data science market is expected to be worth USD 230.80 All these numbers point to one thing–increased job roles and careers, especially when we talk about data engineering jobs in Azure, which are on the rise every year. Let’s get started.

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DataKitchen

JANUARY 25, 2022

When internal resources fall short, companies outsource data engineering and analytics. There’s no shortage of consultants who will promise to manage the end-to-end lifecycle of data from integration to transformation to visualization. . The challenge is that data engineering and analytics are incredibly complex.

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Knowledge Hut

NOVEMBER 17, 2023

Azure Data Engineers play an important role in building efficient, secure, and intelligent data solutions on Microsoft Azure's powerful platform. The position of Azure Data Engineers is becoming increasingly important as businesses attempt to use the power of data for strategic decision-making and innovation.

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Ascend.io

DECEMBER 4, 2024

Another significant challenge is the reactive nature of operations within many data teams. Instead of driving innovation, data engineers often find themselves bogged down with maintenance tasks. On average, engineers spend over half of their time maintaining existing systems rather than developing new solutions.

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Netflix Tech

NOVEMBER 14, 2023

In the previous installments of this series, we introduced Psyberg and delved into its core operational modes: Stateless and Stateful Data Processing. Pipelines After Psyberg Let’s explore how different modes of Psyberg could help with a multistep data pipeline. Stay tuned for a new post on this!

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DataKitchen

APRIL 13, 2021

Composable Analytics — A DataOps Enterprise Platform with built-in services for data orchestration, automation, and analytics. Reflow — A system for incremental data processing in the cloud. Reflow enables scientists and engineers to compose existing tools (packaged in Docker images) using ordinary programming constructs.

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Ascend.io

DECEMBER 4, 2024

Another significant challenge is the reactive nature of operations within many data teams. Instead of driving innovation, data engineers often find themselves bogged down with maintenance tasks. On average, engineers spend over half of their time maintaining existing systems rather than developing new solutions.

Data Pipeline 52

Data Pipeline Metadata Data Workflow Data 52

Ascend.io

OCTOBER 28, 2024

You might even think of effective data transformation like a powerful magnet that draws the needle from the stack, leaving the hay behind. In this blog post, we’ll explore fundamental concepts, intermediate strategies, and cutting-edge techniques that are shaping the future of data engineering.

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Ascend.io

JULY 3, 2024

DataOps, short for Data Operations, integrates data engineering, data quality, and management with agile and DevOps practices. This methodology emphasizes automation, collaboration, and continuous improvement, ensuring faster, more reliable data workflows. Let’s dive in! Key Components of DataOps 1.

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Data Workflow Data Pipeline Data Data Engineering 52

Ascend.io

SEPTEMBER 6, 2024

AI-driven data quality workflows deploy machine learning to automate data cleansing, detect anomalies, and validate data. Integrating AI into data workflows ensures reliable data and enables smarter business decisions. Data quality is the backbone of successful data engineering projects.

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Hevo

AUGUST 16, 2024

This blog explores the world of open source data orchestration tools, highlighting their importance in managing and automating complex data workflows. From Apache Airflow to Google Cloud Composer, we’ll walk you through ten powerful tools to streamline your data processes, enhance efficiency, and scale your growing needs.

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Workfall

JUNE 12, 2023

In this dynamic realm of data engineering, a monumental challenge takes centre stage: efficiently managing the ever-changing tides of real-time data. Data, the lifeblood of organisations, holds the key to unlocking untapped potential and propelling businesses forward. Where Is CDC Used and Who Uses It?

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Databand.ai

AUGUST 30, 2023

DataOps , short for data operations, is an emerging discipline that focuses on improving the collaboration, integration, and automation of data processes across an organization. Accelerated Data Analytics DataOps tools help automate and streamline various data processes, leading to faster and more efficient data analytics.

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Meltano

OCTOBER 5, 2022

In the same way, a DataOps engineer designs the data assembly line that enables data scientists to derive insights from data analytics faster and with fewer errors. DataOps engineers improve the speed and quality of the data development process by applying DevOps principles to data workflow, known as DataOps.

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Workfall

JULY 18, 2023

In the vast realm of data engineering and analytics, a tool emerged that felt like a magical elixir. DBT , the Data Build Tool. Think of DBT as the trusty sidekick that accompanies data analysts and engineers on their quests to transform raw data into golden insights.

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Netflix Tech

NOVEMBER 20, 2023

IPS provides the incremental processing support with data accuracy, data freshness, and backfill for users and addresses many of the challenges in workflows. IPS enables users to continue to use the data processing patterns with minimal changes. Users configure the workflow to read the data in a window (e.g.

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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.

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Ascend.io

JULY 11, 2024

It enhances data quality, governance, and optimization, making data retrieval more efficient and enabling powerful automation in data engineering processes. As practitioners using metadata to fuel data teams, we at Ascend understand the critical role it plays in organizing, managing, and optimizing data workflows.

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Databand.ai

AUGUST 30, 2023

DataOps is a collaborative approach to data management that combines the agility of DevOps with the power of data analytics. It aims to streamline data ingestion, processing, and analytics by automating and integrating various data workflows.

Architecture 52

Architecture Data Ingestion Data Governance Data Cleanse 52

Hevo

SEPTEMBER 1, 2024

Managing and orchestrating data workflows efficiently is crucial in today’s data-driven world. As the amount of data constantly increases with each passing day, so does the complexity of the pipelines handling such data processes.

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Monte Carlo

APRIL 28, 2023

DuckDB’s parallel execution capabilities can help DBAs improve the performance of data processing tasks. Researchers : Academics and researchers working with large volumes of data use DuckDB to process and analyze their data more efficiently. What makes DuckDB different?

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Workfall

JULY 4, 2023

Reading Time: 8 minutes In the world of data engineering, a mighty tool called DBT (Data Build Tool) comes to the rescue of modern data workflows. Imagine a team of skilled data engineers on an exciting quest to transform raw data into a treasure trove of insights.

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Ascend.io

DECEMBER 11, 2023

Data orchestration is the process of efficiently coordinating the movement and processing of data across multiple, disparate systems and services within a company. It automates and optimizes data processes, reducing manual effort and the likelihood of errors. But let’s step back for a second.

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Ascend.io

DECEMBER 19, 2023

Snowflake’s Data Marketplace : Enriches data pipelines with external data sources, providing access to a diverse range of datasets and services that can be seamlessly integrated into your analytics and data processing workflows. that you can combine to create custom data workflows.

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ETL Tools IT Data Pipeline Data Warehouse 52

Databand.ai

AUGUST 30, 2023

The core philosophy of DataOps is to treat data as a valuable asset that must be managed and processed efficiently. It emphasizes the importance of collaboration between different teams, such as data engineers, data scientists, and business analysts, to ensure that everyone has access to the right data at the right time.

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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. Diverging Paths: The Key Differences While ETL and data pipelines share common processes, they diverge significantly in their capabilities and attributes.

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Monte Carlo

JULY 27, 2023

The Expertise and Skills You Bring Engage with product owners and development leads to create testing strategies Identify areas of improvement in data quality processes and propose solutions to enhance data accuracy and reliability. Experience with data processing concepts like mapping documents and complex data relationships.

Engineering 52

Engineering Healthcare Data Warehouse Scala 52

Databand.ai

AUGUST 30, 2023

Unified DataOps represents a fresh approach to managing and synchronizing data operations across several domains, including data engineering, data science, DevOps, and analytics. This transformation may involve changes in team structures or processes to promote better communication among different groups.

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Monte Carlo

NOVEMBER 14, 2023

A rich user interface, broad scalability, strong community support, and critical mass adoption from major tech companies have made Apache Airflow an indispensable tool for most data teams. According to one Redditor on r/dataengineering, “Seems like 99/100 data engineering jobs mention Airflow.” Dagster Image courtesy of Dagster.

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AltexSoft

NOVEMBER 7, 2022

You can hardly compare data engineering toil with something as easy as breathing or as fast as the wind. The platform went live in 2015 at Airbnb, the biggest home-sharing and vacation rental site, as an orchestrator for increasingly complex data pipelines. How data engineering works. What is Apache Airflow?

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PostgreSQL Metadata Python MySQL 52

Databand.ai

DECEMBER 29, 2022

She regularly contributes to IBM’s Journey to AI blog and shares her advice on LinkedIn around data strategy, data science, women in AI, data and analytics, data governance, and artificial intelligence. On LinkedIn, he posts regularly about AI, data, data science, data engineering, and machine learning.

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BI Consulting Data Science Data Governance 52

Monte Carlo

JUNE 14, 2023

For example, data may be extracted from multiple sources, reorganized, and joined at different times before ultimately being served to its final destination. Why is data pipeline architecture important? Data then, and even today for some organizations, was primarily hosted in on-premises databases with non-scalable storage.

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