Striim

AUGUST 14, 2024

When implemented effectively, smart data pipelines seamlessly integrate data from diverse sources, enabling swift analysis and actionable insights. They empower data analysts and business users alike by providing critical information while protecting sensitive production systems. What is a Smart Data Pipeline?

Data Pipeline 52

Data Pipeline Data Architecture Transportation 52

Data Engineering Podcast

JUNE 9, 2024

Summary Streaming data processing enables new categories of data products and analytics. Unfortunately, reasoning about stream processing engines is complex and lacks sufficient tooling. Data lakes are notoriously complex. Data lakes are notoriously complex.

Process 147

Process Data Lake High Quality Data Machine Learning 147

Snowflake

JUNE 6, 2024

Faster, easier AI/ML and data engineering workflows Explore, analyze and visualize data using Python and SQL. Discover valuable business insights through exploratory data analysis. Develop scalable data pipelines and transformations for data engineering.

SQL 115

SQL Python Machine Learning Data Workflow 115

Data Engineering Weekly

FEBRUARY 2, 2025

I finally found a good critique that discusses its flaws, such as multi-hop architecture, inefficiencies, high costs, and difficulties maintaining data quality and reusability. The article advocates for a "shift left" approach to data processing, improving data accessibility, quality, and efficiency for operational and analytical use cases.

Data Engineering 57

Data Engineering Data Engineer Engineering Data Lake 57

Monte Carlo

FEBRUARY 8, 2024

Just as a watchmaker meticulously adjusts every tiny gear and spring in harmonious synchrony for flawless performance, modern data pipeline optimization requires a similar level of finesse and attention to detail. Learn how cost, processing speed, resilience, and data quality all contribute to effective data pipeline optimization.

Data Pipeline 52

Data Pipeline AWS Datasets Data 52

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. Now, let’s explore the state of our pipelines after incorporating Psyberg. This ensures that the next instance of the workflow will pick up newer updates.

Metadata 101

Metadata Data Pipeline Scala Data Workflow 101

Ascend.io

MARCH 17, 2023

These engineering functions are almost exclusively concerned with data pipelines, spanning ingestion, transformation, orchestration, and observation — all the way to data product delivery to the business tools and downstream applications. Pipelines need to grow faster than the cost to run them.

Data Pipeline 52

Data Pipeline Datasets Data Software Engineer 52

Ascend.io

MARCH 17, 2023

These engineering functions are almost exclusively concerned with data pipelines, spanning ingestion, transformation, orchestration, and observation — all the way to data product delivery to the business tools and downstream applications. Pipelines need to grow faster than the cost to run them.

Data Pipeline 52

Data Pipeline Datasets Data Software Engineer 52

Ascend.io

AUGUST 18, 2023

This not only jeopardizes the integrity and robustness of production environments but also compounds challenges for both data scientists and engineers. This article delves into the reasons behind our assertion: data science notebooks are not your best choice for production data pipelines. What Are Jupyter Notebooks?

Data Pipeline 52

Data Pipeline Programming Language Data Science Coding 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

Data Engineering Podcast

JANUARY 30, 2022

Matt Harrison is a Python expert with a long history of working with data who now spends his time on consulting and training. Today’s episode is Sponsored by Prophecy.io – the low-code data engineering platform for the cloud. The only thing worse than having bad data is not knowing that you have it.

Data Engineering 100

Data Engineering Data Engineer Engineering Python 100

Monte Carlo

JUNE 14, 2023

In this post, we will help you quickly level up your overall knowledge of data pipeline architecture by reviewing: Table of Contents What is data pipeline architecture? Why is data pipeline architecture important? What is data pipeline architecture? Why is data pipeline architecture important?

Data Pipeline 52

Data Pipeline Architecture Data Lake Data Warehouse 52

Engineering at Meta

FEBRUARY 4, 2025

For each data logs table, we initiate a new worker task that fetches the relevant metadata describing how to correctly query the data. Once we know what to query for a specific table, we create a task for each partition that executes a job in Dataswarm (our data pipeline system).

Accessible 81

Accessible Accessibility Raw Data Data Warehouse 81

DataKitchen

APRIL 13, 2021

Airflow — An open-source platform to programmatically author, schedule, and monitor data pipelines. Apache Oozie — An open-source workflow scheduler system to manage Apache Hadoop jobs. DBT (Data Build Tool) — A command-line tool that enables data analysts and engineers to transform data in their warehouse more effectively.

Consulting 121

Consulting Machine Learning Data Science Data Pipeline 121

Edureka

APRIL 22, 2025

Since all of Fabric’s tools run natively on OneLake, real-time performance without data duplication is possible in Direct Lake mode. Because of the architecture’s ability to abstract infrastructure complexity, users can focus solely on data workflows.

BI 52

BI Pipeline-centric Data Lake Google Cloud 52

Data Engineering Podcast

AUGUST 28, 2022

Sign up free… or just get the free t-shirt for being a listener of the Data Engineering Podcast at dataengineeringpodcast.com/rudder. The only thing worse than having bad data is not knowing that you have it. Bigeye let’s data teams measure, improve, and communicate the quality of your data to company stakeholders.

Data Engineering 100

Data Engineering Data Engineer MongoDB Metadata 100

Ascend.io

JULY 3, 2024

This methodology emphasizes automation, collaboration, and continuous improvement, ensuring faster, more reliable data workflows. With data workflows growing in scale and complexity, data teams often struggle to keep up with the increasing volume, variety, and velocity of data. Let’s dive in!

Data Workflow 52

Data Workflow Data Pipeline Data Data Engineering 52

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. Note that the backfill support is skipped in this blog.

Process 87

Process Data Pipeline Datasets SQL 87

DataKitchen

JANUARY 25, 2022

DataOps improves the robustness, transparency and efficiency of data workflows through automation. For example, DataOps can be used to automate data integration. Previously, the consulting team had been using a patchwork of ETL to consolidate data from disparate sources into a data lake.

Consulting 97

Consulting Recruitment Data Lake Data Engineering 97

Ascend.io

JULY 3, 2024

Start small, then scale With data workflows growing in scale and complexity, data teams often struggle to keep up with the increasing volume, variety, and velocity of data. This is where DataOps comes ina methodology designed to streamline and automate data workflows, ensuring faster and more reliable data delivery.

Data Workflow 52

Data Workflow Data Pipeline Data Data Engineering 52

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. Each type of tool plays a specific role in the DataOps process, helping organizations manage and optimize their data pipelines more effectively.

Data Cleanse 52

Data Cleanse Data Pipeline Data Ingestion Data Validation 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.

Data Workflow 52

Data Workflow Data Pipeline Data Data Process 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.

Data Cleanse 52

Data Cleanse Data Workflow Data Pipeline Machine Learning 52

Snowflake

JULY 22, 2024

From there, you can address more complex use cases, such as creating a 360-degree view of customers by integrating systems across CRM, ERP, marketing applications, social media handles and other data sources. This smoothes out workflows and helps teams swiftly mitigate potential issues.

Government 119

Government Data Cloud Hadoop 119

Ascend.io

DECEMBER 11, 2023

Here’s the deal: for data to truly drive your business forward, you need a reliable and scalable system to keep it moving without hiccups. In other words, you need data orchestration. In this article, we’ll break down what data orchestration is, its significance, and how it differs from data pipeline orchestration.

Data Workflow 52

Data Workflow Data Pipeline Data Lake Data 52

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

Cloudyard

JUNE 11, 2024

Read Time: 1 Minute, 48 Second RETRY LAST: In modern data workflows, tasks are often interdependent, forming complex task chains. Ensuring the reliability and resilience of these workflows is critical, especially when dealing with production data pipelines.

Raw Data 52

Raw Data Aggregated Data Data Pipeline Data Workflow 52

Monte Carlo

JULY 9, 2024

Overcoming Challenges to Achieve Data Timeliness Despite the clear benefits of timely data, organizations often encounter several hurdles: Complex Data Pipelines: Intricate data pipelines that involve multiple stages and dependencies can hinder smooth data flow.

Food 52

Food Data Pipeline Data Data Workflow 52

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.

Engineering 52

Engineering Raw Data Data Pipeline Data Warehouse 52

Databand.ai

AUGUST 30, 2023

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. This includes data ingestion, processing, storage, and analysis.

Data Governance 52

Data Governance Data Pipeline Government Business Analyst 52

Ascend.io

DECEMBER 19, 2023

That’s what we call a data pipeline. It could just as well be ‘ELT for Snowflake’ The key takeaway is that these terms are representative of the actual activity being undertaken: the construction and management of data pipelines within the Snowflake environment.

ETL Tools 52

ETL Tools IT Data Pipeline Data Warehouse 52

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.

Metadata 52

Metadata IT Government 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. What is the role of a Data Engineer? They are also accountable for communicating data trends.

Data Engineering 98

Data Engineering Data Engineer Engineering Hadoop 98

Ascend.io

JUNE 10, 2024

At Ascend, we recognize the immense potential in integrating these advanced AI capabilities into our platform, enabling smarter applications and more efficient data workflows. Snowflake’s investment in expanding data engineering capabilities is a game-changer. Curious to see Cortex in Action?

Data Engineering 52

Data Engineering Data Engineer Engineering High Quality Data 52

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?

Data Warehouse 52

Data Warehouse Programming Language Database Datasets 52

Knowledge Hut

SEPTEMBER 25, 2023

As an Azure Data Engineer, you will be expected to design, implement, and manage data solutions on the Microsoft Azure cloud platform. You will be in charge of creating and maintaining data pipelines, data storage solutions, data processing, and data integration to enable data-driven decision-making inside a company.

Data Engineering 52

Data Engineering Data Engineer Engineering Data Lake 52

Workfall

JUNE 12, 2023

and we have now migrated the data from our transactional database to the Snowflake data warehouse. Now we can easily create data streams and create a CDC mechanism in Snowflake. The success is true so let’s now verify this change in our snowflake table.

Telecommunication 80

Telecommunication Metadata Healthcare Finance 80