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

JULY 17, 2022

How does that play into the broader conversation about assessing the value of a given data-set? How have the design and goals of the platform changed or evolved since you started working on it? How does that play into the broader conversation about assessing the value of a given data-set?

Data Management 130

Data Management Management Metadata MongoDB 130

Knowledge Hut

SEPTEMBER 25, 2023

You can learn deeply about the Azure learning journey that leads to the position of an Azure Data Engineer Associate on the Microsoft Azure platform. Who is an Azure Data Engineer? As an Azure Data Engineer, you will be expected to design, implement, and manage data solutions on the Microsoft Azure cloud platform.

Data Engineering 52

Data Engineering Data Engineer Engineering Data Lake 52

François Nguyen

MARCH 22, 2021

TL;DR After setting up and organizing the teams, we are describing 4 topics to make data mesh a reality. As you can see, this is in the code part where you are building your data pipelines, a misnomer because this is an over simplification. The other benefit is you can also use parameters and build a generic workflows to be re-used.

Architecture 130

Architecture Technology Google Cloud Metadata 130

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 Engineering 52

Data Engineering Data Engineer Engineering Data Warehouse 52

Data Engineering Podcast

APRIL 5, 2021

She explains how the design of the platform is informed by the needs of managing data projects for large and small teams across her previous roles, how it integrates with your existing systems, and how it can work to bring everyone onto the same page. What portions of the data workflow is Atlan responsible for?

Data Warehouse 100

Data Warehouse Data Pipeline BI Metadata 100

Christophe Blefari

SEPTEMBER 28, 2023

Historically, data pipelines were designed with an ETL approach, storage was expensive and we had to transform the data before using it. With the cloud, we got the—false—impression that resources were infinite and cheap, so we switched to ETL by pushing everything into a central data storage.

Cloud Storage 147

Cloud Storage Big Data Hadoop SQL 147

DataKitchen

MAY 10, 2024

How DataKitchen Solves Data Production Challenges DataKitchen’s DataOps Observability tackles these challenges head-on with its innovative DataOps TestGen software, which automates the generation of 32 distinct data quality validation tests.

Raw Data 40

Raw Data Data Ingestion Datasets Data 40

Data Engineering Podcast

OCTOBER 22, 2021

In this episode Oliver Laslett describes why dashboards aren’t sufficient for business analytics, how Lightdash promotes the work that you are already doing in your data warehouse modeling with dbt, and how they are focusing on bridging the divide between data teams and business teams and the requirements that they have for data workflows.

Engineering 100

Engineering Business Intelligence BI Data Warehouse 100

Data Engineering Podcast

JULY 31, 2022

Your host is Tobias Macey and today I’m interviewing Ernie Ostic about Manta, an automated data lineage service for managing visibility and quality of your data workflows Interview Introduction How did you get involved in the area of data management? How have the design and goals of the product changed or evolved?

IT 100

IT Metadata MongoDB MySQL 100

Data Engineering Podcast

MAY 3, 2021

Summary The Data industry is changing rapidly, and one of the most active areas of growth is automation of data workflows. Taking cues from the DevOps movement of the past decade data professionals are orienting around the concept of DataOps. How does security factor into the design of robust DataOps systems?

Data Warehouse 100

Data Warehouse Data Pipeline BI Metadata 100

Data Engineering Weekly

DECEMBER 29, 2022

Data catalogs are the most expensive data integration systems you never intended to build. Data Catalog as a passive web portal to display metadata requires significant rethinking to adopt modern data workflow, not just adding “modern” in its prefix. How happy are you with your data catalogs?

Metadata 62

Metadata Data Warehouse ETL Tools Data Workflow 62

ThoughtSpot

SEPTEMBER 6, 2023

The pathway from ETL to actionable analytics can often feel disconnected and cumbersome, leading to frustration for data teams and long wait times for business users. And even when we manage to streamline the data workflow, those insights aren’t always accessible to users unfamiliar with antiquated business intelligence tools.

Data Workflow 91

Data Workflow Raw Data Data Lake Business Intelligence 91

Data Engineering Podcast

OCTOBER 15, 2021

Summary The binding element of all data work is the metadata graph that is generated by all of the workflows that produce the assets used by teams across the organization. The DataHub project was created as a way to bring order to the scale of LinkedIn’s data needs. How is the governance of DataHub being managed?

Metadata 100

Metadata BI Data Warehouse Government 100

Monte Carlo

FEBRUARY 8, 2024

Here are some strategies to keep things firing on all cylinders: Parallelize data processing: Divide large data tasks into smaller, manageable chunks that can be processed concurrently. Identify parts of your data workflow that can be parallelized and adjust your pipeline configuration accordingly.

Data Pipeline 52

Data Pipeline AWS Datasets Data 52

Knowledge Hut

NOVEMBER 2, 2023

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. A Data Engineer is responsible for designing the entire architecture of the data flow while taking the needs of the business into account.

Data Engineering 52

Data Engineering Data Engineer Coding Project 52

Monte Carlo

JULY 27, 2023

The data quality engineer will then need to design or optimize data architectures and pipelines to ensure these requirements are met (mentioned in 70% of job postings). This is a continuous process as data environments are complex, interdependent, and constantly changing. Ability to perform root cause analysis on defects.

Engineering 52

Engineering Healthcare Scala Data Warehouse 52

Knowledge Hut

NOVEMBER 17, 2023

Their job entails Azure data engineer skills like using big data, databases, data lakes, and analytics to help firms make efficient data-driven decisions. The DP 203 syllabus is designed by incorporating real-world business case studies and activities, you can make sure that your abilities are applicable to the workplace.

Data Engineering 52

Data Engineering Data Engineer Engineering Data Lake 52

Ascend.io

DECEMBER 19, 2023

The GUI often allows for easy visualization and monitoring of data pipelines, which can be particularly beneficial for complex workflows. However, there is a critical aspect to consider: using Snowflake’s native features to build data pipelines is akin to constructing a bespoke data platform.

ETL Tools 52

ETL Tools IT Data Pipeline Data Warehouse 52

Knowledge Hut

DECEMBER 28, 2023

This blog will guide us through the Azure Data Engineer certification path , equipping us with insights necessary for this transformative journey. Who is an Azure Data Engineer? An Azure Data Engineer is responsible for designing, implementing and managing data solutions on Microsoft Azure.

Certification 52

Certification Data Engineering Data Engineer Engineering 52

DataKitchen

FEBRUARY 27, 2024

Embracing DataOps for Enhanced Data Journey Management The complexity of managing Data Journeys, especially in RAG and LLMs, underscores the importance of embracing DataOps principles. DataOps provides a framework for automating and optimizing data workflows, emphasizing collaboration, monitoring, and continuous improvement.

Data Engineering 48

Data Engineering Data Engineer Engineering Unstructured Data 48

Data Engineering Podcast

AUGUST 5, 2019

Finding the data that you need is tricky, and Amundsen will help you solve that problem. And as your data grows in volume and complexity, there are foundational principles that you can follow to keep data workflows streamlined. Can you start by explaining what Amundsen is and the problems that it was designed to address?

PostgreSQL 100

PostgreSQL MongoDB Metadata Media 100

Knowledge Hut

SEPTEMBER 29, 2023

Why Should You Get an Azure Data Engineer Certification? Becoming an Azure data engineer allows you to seamlessly blend the roles of a data analyst and a data scientist. One of the pivotal responsibilities is managing data workflows and pipelines, a core aspect of a data engineer's role.

Certification 52

Certification Data Engineering Data Engineer Engineering 52

Hepta Analytics

FEBRUARY 14, 2022

We already had a script that downloaded a csv file, processed the data and pushed the data to postgres database. This week, we got to think about our data ingestion design. The data is in use and is difficult to update. Designing the workflow is dependent on the use case. It is called Idempotency.

Data Ingestion 52

Data Ingestion Data Engineering Data Engineer Engineering 52

Monte Carlo

SEPTEMBER 20, 2022

Here’s how Prefect , Series B startup and creator of the popular data orchestration tool, harnessed the power of data observability to preserve headcount, improve data quality and reduce time to detection and resolution for data incidents. This left Dylan’s team with a gap to fill.

Big Data 52

Big Data Data Warehouse Data Data Governance 52

Data Engineering Podcast

MARCH 31, 2024

Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management Data lakes are notoriously complex. Datafold has recently launched data replication testing, providing ongoing validation for source-to-target replication. How is Elementary designed/implemented?

Project 130

Project Data Lake High Quality Data Data Workflow 130

Workfall

JUNE 12, 2023

It facilitates data synchronisation, replication, real-time analytics, and event-driven processing, empowering data-driven decision-making and operational efficiency. Types of CDC Audit Columns: This method involves using designated columns within tables to track incremental changes.

Telecommunication 80

Telecommunication Metadata Healthcare Finance 80

Data Engineering Podcast

MAY 12, 2024

Data lakes are notoriously complex. For data engineers who battle to build and scale high quality data workflows on the data lake, Starburst is an end-to-end data lakehouse platform built on Trino, the query engine Apache Iceberg was designed for, with complete support for all table formats including Apache Iceberg, Hive, and Delta Lake.

Management 130

Management Data Lake High Quality Data Machine Learning 130

DataKitchen

JANUARY 3, 2022

The key to enabling rapid, iterative development is through a process hub , which complements and automates the data hub. A process hub, in essence, automates every possible workflow related to the data hub. DataOps Engineering is about designing the system that creates analytics more robustly and efficiently.

Business Analyst 98

Business Analyst Data Lake Consulting Data Analytics 98

Cloudera

AUGUST 21, 2020

This type of end-to-end data processing that starts at the Edge and ends in the cloud is made possible by using Apache NiFi. NiFi is software from the Apache Software Foundation which is designed to help the flow of data through an organization. This can extend to streaming analytics capabilities into any cloud environment.

Banking 61

Banking Kafka Cloud Storage Government 61

Data Engineering Podcast

APRIL 21, 2024

Data lakes are notoriously complex. For data engineers who battle to build and scale high quality data workflows on the data lake, Starburst powers petabyte-scale SQL analytics fast, at a fraction of the cost of traditional methods, so that you can meet all your data needs ranging from AI to data applications to complete analytics.

Data Lake 182

Data Lake High Quality Data Data Pipeline Machine Learning 182

Data Engineering Podcast

FEBRUARY 4, 2024

Summary Stream processing systems have long been built with a code-first design, adding SQL as a layer on top of the existing framework. In this episode Yingjun Wu explains how it is architected to power analytical workflows on continuous data flows, and the challenges of making it responsive and scalable.

SQL 173

SQL Data Lake High Quality Data Data Pipeline 173

ProjectPro

DECEMBER 21, 2021

Loan Eligibility Prediction Project This intermediate-level project will teach you machine learning aspects such as feature engineering , performing in-depth exploratory data analysis, etc. You will train and test the data model using the cross-validation method. and performs data analysis and data cleaning activities, etc.

Machine Learning 52

Machine Learning Engineering Algorithm Computer Science 52

Cloudera

FEBRUARY 11, 2019

Cloudera champions powerful data analytics anywhere, empowering people with robust data analytics at scale – streaming, SQL, transactional, data science, machine learning and AI – with an experience that improves individual and team innovation and productivity.

Data Management 43

Data Management Management Metadata Government 43

Data Engineering Podcast

FEBRUARY 18, 2024

Data lakes are notoriously complex. For data engineers who battle to build and scale high quality data workflows on the data lake, Starburst powers petabyte-scale SQL analytics fast, at a fraction of the cost of traditional methods, so that you can meet all your data needs ranging from AI to data applications to complete analytics.

Data Lake 262

Data Lake High Quality Data Data Warehouse Google Cloud 262

Data Engineering Podcast

JANUARY 28, 2024

Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management Data lakes are notoriously complex. SIEM) A query engine is useless without data to analyze. What are the data acquisition paths/sources that you are designed to work with?-

Data Lake 147

Data Lake Building High Quality Data AWS 147

dbt Developer Hub

MARCH 23, 2023

Easy and intuitive code : Because audit_helper relies on dbt macros, it was designed to be an intuitive tool that runs on simple SQL queries. Among the benefits, we can mention: Quality assurance : Assert that a refactored model is reaching the same output as the original model that is being refactored.

Data Warehouse 64

Data Warehouse Generalist SQL Coding 64