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

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 Engineer 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

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

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

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 96

SQL Python Machine Learning Data Workflow 96

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 99

Metadata Data Pipeline Scala Data Workflow 99

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

Ascend.io

OCTOBER 28, 2024

Tools like Python’s requests library or ETL/ELT tools can facilitate data enrichment by automating the retrieval and merging of external data. Read More: Discover how to build a data pipeline in 6 steps Data Integration Data integration involves combining data from different sources into a single, unified view.

Raw Data 52

Raw Data Datasets Aggregated Data Data Pipeline 52

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

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

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

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

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 Engineer 100

Data Engineer Data Engineering Engineering Python 100

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

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 Pipeline 52

Data Pipeline Data Governance Government Business Analyst 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 Engineer 100

Data Engineer Data Engineering MongoDB Metadata 100

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 86

Process Data Pipeline Datasets SQL 86

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 Engineer 52

Data Engineer Data Engineering Engineering High Quality Data 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 ETL Tools 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 Engineer 52

Data Engineer Data Engineering Engineering Data Lake 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 Datasets Database 52

Knowledge Hut

NOVEMBER 2, 2023

Who is Azure Data Engineer? An Azure Data Engineer is a professional who is in charge of designing, implementing, and maintaining data processing systems and solutions on the Microsoft Azure cloud platform. Azure SQL Database, Azure Data Lake Storage). Azure SQL Database, Azure Data Lake Storage).

Data Engineer 52

Data Engineer Data Engineering Project Coding 52

Knowledge Hut

NOVEMBER 17, 2023

The job of an Azure Data Engineer is really needed in the world of handling and studying data. As Azure Data Engineers, they'll be responsible for creating and looking after solutions that use data to help the company. Azure Data Factory stands at the forefront, orchestrating data workflows.

Data Engineer 52

Data Engineer Data Engineering Engineering Data Lake 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. Databricks Workflows Image courtesy of Databricks. Just smooth, seamless operations. Dagster Image courtesy of Dagster.

Pipeline-centric 40

Pipeline-centric Google Cloud Python Data Workflow 40

Databand.ai

AUGUST 30, 2023

These experts will need to combine their expertise in data processing, storage, transformation, modeling, visualization, and machine learning algorithms, working together on a unified platform or toolset.

Data Governance 40

Data Governance Data Cleanse Government Data Science 40

Monte Carlo

MAY 14, 2024

Apache ORC (Optimized Row Columnar) : In 2013, ORC was developed for the Hadoop ecosystem to improve the efficiency of data storage and retrieval. This development was crucial for enabling both batch and streaming data workflows in dynamic environments, ensuring consistency and durability in big data processing.

Data Lake 52

Data Lake Metadata Hadoop Data Governance 52

DataKitchen

MAY 10, 2024

The Five Use Cases in Data Observability: Mastering Data Production (#3) Introduction Managing the production phase of data analytics is a daunting challenge. Overseeing multi-tool, multi-dataset, and multi-hop data processes ensures high-quality outputs.

Raw Data 50

Raw Data Data Ingestion Datasets Data 50

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 Engineer 97

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

Knowledge Hut

DECEMBER 28, 2023

An Azure Data Engineer is responsible for designing, implementing and managing data solutions on Microsoft Azure. The Azure Data Engineer certification imparts to them a deep understanding of data processing, storage and architecture. It also shows that they can manage data workflows across various Azure services.

Certification 52

Certification Data Engineer Data Engineering Engineering 52

Knowledge Hut

MARCH 28, 2024

Job Role 1: Azure Data Engineer Azure Data Engineers develop, deploy, and manage data solutions with Microsoft Azure data services. They use many data storage, computation, and analytics technologies to develop scalable and robust data pipelines.

Data Engineer 52

Data Engineer Data Engineering Engineering Data Warehouse 52

Monte Carlo

JULY 27, 2023

Data quality engineers also need to have experience operating in cloud environments and using many of the modern data stack tools that are utilized in building and maintaining data pipelines. 78% of job postings referenced at least part of their environment was in a modern data warehouse, lake, or lakehouse.

Engineering 52

Engineering Healthcare Data Warehouse Scala 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

AltexSoft

NOVEMBER 7, 2022

The platform went live in 2015 at Airbnb, the biggest home-sharing and vacation rental site, as an orchestrator for increasingly complex data pipelines. It still remains a leading workflow management tool adopted by thousands of companies, from tech giants to startups. How data engineering works. What is Apache Airflow?

Metadata 52

Metadata PostgreSQL Python MySQL 52

Knowledge Hut

MAY 23, 2024

Users can also leverage it for generating interactive visualizations over data. It also comes with lots of automation techniques that qualify users to eliminate manual data workflows. With this tool, data science professionals can quickly extract and transform data. Big Data Tools 23.

Data Science 52

Data Science MongoDB Programming Language Hadoop 52