Data Engineering Podcast

AUGUST 28, 2022

Push information about data freshness and quality to your business intelligence, automatically scale up and down your warehouse based on usage patterns, and let the bots answer those questions in Slack so that the humans can focus on delivering real value.

Data Engineering 100

Data Engineering Data Engineer MongoDB Metadata 100

Data Engineering Podcast

JULY 17, 2022

Push information about data freshness and quality to your business intelligence, automatically scale up and down your warehouse based on usage patterns, and let the bots answer those questions in Slack so that the humans can focus on delivering real value.

Data Management 130

Data Management Management Metadata MongoDB 130

Data Engineering Podcast

NOVEMBER 27, 2021

In the same way that application performance monitoring ensures reliable software and keeps application downtime at bay, Monte Carlo solves the costly problem of broken data pipelines. Start trusting your data with Monte Carlo today! What are some examples of data sets that an organization might want to use in their analytics?

Data Warehouse 100

Data Warehouse Data Lake BI Business Intelligence 100

Data Engineering Podcast

JANUARY 1, 2022

In the same way that application performance monitoring ensures reliable software and keeps application downtime at bay, Monte Carlo solves the costly problem of broken data pipelines. Start trusting your data with Monte Carlo today! To what extent do speed benchmarks inform decisions for modern data teams?

Data Warehouse 100

Data Warehouse SQL Hadoop Data Lake 100

Data Engineering Podcast

JULY 31, 2022

Push information about data freshness and quality to your business intelligence, automatically scale up and down your warehouse based on usage patterns, and let the bots answer those questions in Slack so that the humans can focus on delivering real value. Can you describe what Manta is and the story behind it?

IT 100

IT Metadata MongoDB MySQL 100

Data Engineering Podcast

SEPTEMBER 18, 2022

Push information about data freshness and quality to your business intelligence, automatically scale up and down your warehouse based on usage patterns, and let the bots answer those questions in Slack so that the humans can focus on delivering real value. What is the core problem that you are trying to solve at Workstream?

Building 100

Building Metadata MongoDB MySQL 100

dbt Developer Hub

APRIL 24, 2023

One example of a popular drag-and-drop transformation tool is Alteryx which allows business analysts to transform data by dragging and dropping operators in a canvas. In this sense, dbt may be a more suitable solution to building resilient and modular data pipelines due to its focus on data modeling.

BI 83

BI Data Workflow SQL Data Pipeline 83

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. Let us first get a clear understanding of why Data Science is important. What is the need for Data Science?

Data Engineering 98

Data Engineering Data Engineer Engineering Hadoop 98

Knowledge Hut

SEPTEMBER 27, 2023

In the current data-driven environment, starting a business intelligence (BI) journey has become crucial. Come explore with me the special features that set Power BI apart from other business intelligence tools in the current day. Power BI is a robust business intelligence (BI) platform developed by Microsoft.

BI 52

BI Business Intelligence Certification Data Security 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. Common solutions include AWS S3 , Azure Data Lake , and Google Cloud Storage.

Designing 52

Designing Data Engineering Data Engineer Engineering 52

Meltano

JULY 7, 2022

The modern data stack and data lifecycle are extremely complex. An ideal data stack today can have upwards of 15 tools in it, including extract and loader tools, transformers, reverse ETL, warehouses, business intelligence (BI) tools, and metrics layers. Data is just very different from dev.

Software Engineer 52

Software Engineer Software Engineering BI Data Workflow 52

Hepta Analytics

FEBRUARY 14, 2022

This means that your data store has to have capabilities of transforming the data. It is mostly used for big data. The data ingestion takes less time compared to ETL. It is also preferred when the use case has more diverse business intelligence. The purpose of the data is not determined.

Data Ingestion 52

Data Ingestion Data Engineering Data Engineer Engineering 52

Monte Carlo

APRIL 24, 2023

While we won’t get into the minutia of every consideration for every level of the data stack, it’s important to recall these five considerations as they’ll nonetheless steer the direction of our conversation. 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

Striim

SEPTEMBER 11, 2024

Data Warehouses: These are optimized for storing structured data, often organized in relational databases. They support complex querying and analytical processing, making them ideal for business intelligence and reporting.

Data Pipeline 98

Data Pipeline Designing Data Lake Data Warehouse 98

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. After Python and R, it is the most popular data science programming language that is gaining momentum and popularity among data scientists.

Data Science 52

Data Science MongoDB Programming Language Hadoop 52

Meltano

FEBRUARY 22, 2023

Testing a data application is similar to testing any software application in many ways, just with a strong focus on testing data-related issues. But testing problems like failing data workflows, mismatches in data reconciliation after ETL, and data quality issues means that you’re not only testing the code but also the data itself.

Data 52

Data Transportation Data Workflow Data Lake 52

AltexSoft

MARCH 14, 2023

Data uses Here comes why you need this whole MDS thing in the first place — the data use component, or how the data is actually utilized. There are two main areas of use within this component: the first is data analytics and business intelligence and the second is data science.

IT 59

IT Data Warehouse Data Governance Data Lake 59

Monte Carlo

JANUARY 16, 2024

Factor in the advertising strategies, media production, partner programming, audience analytics…and you’re looking at an ocean of data that would fill even the deepest trench (we’d like a television show about that too, please!). So how does Fox’s data strategy support these complex data workflows?

Government 52

Government High Quality Data Data Governance Entertainment 52

Databand.ai

DECEMBER 29, 2022

Her proprietary methodology fuses the art of data storytelling and visualization to drive users to take action. Mico’s ability to help companies gain ROI from their business intelligence investments has been sought out by Fortune 500 companies. Mico actively posts about data analytics, BI strategy, and data storytelling.

BI 52

BI Consulting Data Science Data Governance 52

Monte Carlo

MARCH 24, 2023

Sure, terabytes or even petabytes of data are involved, but generally it’s not the size of the data but everything surrounding the data–workflows, access permissions, layers of dependencies–that pose data migration risks. In reality, it’s never quite that simple.

Data Warehouse 52

Data Warehouse AWS Cloud Database 52

Knowledge Hut

DECEMBER 28, 2023

This certification, often referred to as the Azure Data Engineer Associate certification, validates the competency of individuals in implementing Azure data solutions. It’s a testament to their ability to create scalable, efficient and secure data pipelines. What is the Azure Data Engineer Certification?

Certification 52

Certification Data Engineering Data Engineer 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

phData: Data Engineering

MAY 11, 2023

The highly repeatable and uni-directional flow of data means that a simple approach can be taken with analytics while still maintaining basic rigor to protect the integrity of the workflows built. In addition, breaking the human and data workflows into modular components enables this simplification.

Coding 52

Coding Software Engineer Software Engineering Project 52

AltexSoft

SEPTEMBER 21, 2023

The Elastic Stacks Elasticsearch is integral within analytics stacks, collaborating seamlessly with other tools developed by Elastic to manage the entire data workflow — from ingestion to visualization. Accessible via a unified API, these new features enhance search relevance and are available on Elastic Cloud.

Engineering 52

Engineering NoSQL Programming Language Java 52

Data Engineering Podcast

MAY 18, 2024

Summary The purpose of business intelligence systems is to allow anyone in the business to access and decode data to help them make informed decisions. Unfortunately this often turns into an exercise in frustration for everyone involved due to complex workflows and hard-to-understand dashboards.

Building 278

Building Data Lake High Quality Data Business Intelligence 278

phData: Data Engineering

JANUARY 3, 2022

This commonly introduces: Database or Data Warehouse API/EDI Integrations ETL software Business intelligence tooling By leveraging off-the-shelf tooling, your company separates disciplines by technology. This helps drive requirements and determines the right validation at the right time for the data.

IT 52

IT AWS Software Engineer Software Engineering 52