Meltano

OCTOBER 5, 2022

What Is Data Engineering? Data engineering is the process of designing systems for collecting, storing, and analyzing large volumes of data. Put simply, it is the process of making raw data usable and accessible to data scientists, business analysts, and other team members who rely on data.

Data Engineer 52

Data Engineer Data Engineering Engineering Raw Data 52

Monte Carlo

FEBRUARY 25, 2025

Data engineering design patterns are repeatable solutions that help you structure, optimize, and scale data processing, storage, and movement. They make data workflows more resilient and easier to manage when things inevitably go sideways. Thats why solid design patterns matter. Which One Should You Choose?

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Designing Data Engineering Data Engineer Engineering 52

Striim

SEPTEMBER 11, 2024

Third-Party Data: External data sources that your company does not collect directly but integrates to enhance insights or support decision-making. These data sources serve as the starting point for the pipeline, providing the raw data that will be ingested, processed, and analyzed.

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Data Pipeline Designing Data Lake Data Warehouse 98

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

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? Some of the most common responsibilities are as follows: 1.

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Data Engineer Data Engineering Engineering Hadoop 98

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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Datasets Entertainment Data Data Workflow 77

Ripple Engineering

MAY 27, 2021

At Ripple , we are moving towards building complex business models out of raw data. A prime example of this was the process of managing our data transformation workflows. This enables our analysts to focus on data curation and modelling rather than infrastructure. SQL Models A model is a single.sql file.

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Building Raw Data SQL Data 52

DataKitchen

MAY 10, 2024

Data Migration : This use case focuses on verifying data accuracy during migration projects, such as cloud transitions, to ensure that migrated data matches the legacy data regarding output and functionality. Are all required data records and values present and accurate?

Raw Data 50

Raw Data Data Ingestion Datasets Data 50

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

Ascend.io

DECEMBER 11, 2023

In comparison, general data orchestration does not offer this degree of contextual insight Why Data Orchestration Is Important (But an Unnecessary Complication?) Not every team needs data orchestration. However, this approach quickly shows its limitations as data volume escalates. So, why is data orchestration a big deal?

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Data Workflow Data Pipeline Data Lake Data 52

Hepta Analytics

FEBRUARY 14, 2022

When the business intelligence needs change, they can go query the raw data again. ELT: source Data Lake vs Data Warehouse Data lake stores raw data. The purpose of the data is not determined. The data is easily accessible and is easy to update. It is called Idempotency.

Data Ingestion 52

Data Ingestion Data Engineer Data Engineering Engineering 52

Ascend.io

JULY 11, 2024

Metadata is the information that provides context and meaning to data, ensuring it’s easily discoverable, organized, and actionable. It enhances data quality, governance, and automation, transforming raw data into valuable insights. This is what managing data without metadata feels like. Chaos, right?

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Metadata IT Government High Quality Data 52

AltexSoft

MARCH 14, 2023

Data storage The tools mentioned in the previous section are instrumental in moving data to a centralized location for storage, usually, a cloud data warehouse, although data lakes are also a popular option. But this distinction has been blurred with the era of cloud data warehouses.

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IT Data Warehouse Data Governance Data Lake 59

Snowflake

MARCH 27, 2023

It seems everyone has a handful of such shapes in their raw data, and in the past they had to fix those shapes outside of Snowflake before ingesting them. Workflows automates not only geospatial processes, but other data workflows as well.

Generalist 52

Generalist Raw Data Business Analyst SQL 52

Data Engineering Weekly

JANUARY 15, 2023

. 🎯 I defined the modern data stack sometime back as; @sarahmk125 MDS is a set of vendor tools that solve niche data problems (lineage, orchestration, quality) with the side effect of creating a disjointed data workflow that makes data folks lives more complicated.","username":"ananthdurai","name":"at-ananth-at-data-folks

Data Engineer 52

Data Engineer Data Engineering Engineering Metadata 52

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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Data Warehouse SQL Database PostgreSQL 69

ProjectPro

JANUARY 20, 2016

Apache Pig helps SQL server professionals create parallel data workflows. Apache pig eases data manipulation over multiple data sources using a combination of tools. Professionals familiar with using SQL server integration services (SSIS) know the difficulty in making SSIS operations run across multiple CPU cores.

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Hadoop SQL Java Big Data 52

phData: Data Engineering

SEPTEMBER 29, 2023

In reality, though, if you use data (read: any information), you are most likely practicing some form of data engineering every single day. Classically, data engineering is any process involving the design and execution of systems whose primary purpose is collecting and preparing raw data for user consumption.

Data Engineer 52

Data Engineer Data Engineering Engineering Algorithm 52

Monte Carlo

APRIL 24, 2023

Data ingestion When we think about the flow of data in a pipeline, data ingestion is where the data first enters our platform. There are two primary types of raw data. It required a complete data workflow and an orchestration team that’s frankly not feasible for most organizations.

Data Pipeline 52

Data Pipeline Building Data Ingestion BI 52

Edureka

JULY 5, 2024

Tableau Prep has brought in a new perspective where novice IT users and power users who are not backward faithfully can use drag and drop interfaces, visual data preparation workflows, etc., simultaneously making raw data efficient to form insights. Directly visualizes and analyzes prepared data.

Data Preparation 40

Data Preparation Process BI ETL Tools 40

Edureka

JULY 3, 2024

Companies are drowning in a sea of raw data. As data volumes explode across enterprises, the struggle to manage, integrate, and analyze it is getting real. Thankfully, with serverless data integration solutions like Azure Data Factory (ADF), data engineers can easily orchestrate, integrate, transform, and deliver data at scale.

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Pipeline-centric Data Lake Database-centric Data Pipeline 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. The goal of this strategy is to streamline the entire process of extracting insights from raw data by removing silos between teams and technologies.

Data Governance 40

Data Governance Data Cleanse Government Data Science 40

Knowledge Hut

DECEMBER 28, 2023

The Azure Data Engineer certification imparts to them a deep understanding of data processing, storage and architecture. By leveraging their proficiency, they enable organizations to transform raw data into valuable insights that drive business decisions. What is the Azure Data Engineer Certification?

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Certification Data Engineer Data Engineering Engineering 52

Monte Carlo

JUNE 14, 2023

Apache Spark – Labeled as a unified analytics engine for large scale data processing, many leverage this open source solution for streaming use cases, often in conjunction with Databricks. Data orchestration Airflow : Airflow is the most common data orchestrator used by data teams.

Data Pipeline 52

Data Pipeline Architecture Data Lake Data Warehouse 52

DataKitchen

MARCH 16, 2023

Look at the world from your customer perspective – the dashboard is wrong, the data is late, or the numbers ‘look funny.’ Additionally, investing in data testing and observability and implementing DataOps principles can help streamline data workflows and reduce the time it takes to complete data analytics projects.

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Data Analytics Manufacturing Data Process 52

Cloudera

DECEMBER 4, 2024

For example: An AWS customer using Cloudera for hybrid workloads can now extend analytics workflows to Snowflake, gaining deeper insights without moving data across infrastructures. Or now customers can combine Cloudera’s raw data processing and Snowflake’s analytical capabilities to build efficient AI/ML pipelines.

AWS 89

AWS Raw Data Relational Database Government 89

Striim

MARCH 13, 2025

These compact numerical representations of data not only facilitate enhanced data analysis but also improve search capabilities and machine learning tasks. Striim has harnessed the power of vector embeddings to provide its customers with a robust tool for transforming raw data into meaningful insights in real-time.

Data Process 52

Data Process Process Raw Data Machine Learning 52