Databand.ai

JULY 19, 2023

Complete Guide to Data Ingestion: Types, Process, and Best Practices Helen Soloveichik July 19, 2023 What Is Data Ingestion? Data Ingestion is the process of obtaining, importing, and processing data for later use or storage in a database. In this article: Why Is Data Ingestion Important?

Data Ingestion 52

Data Ingestion Process Data Cleanse Data Governance 52

Ascend.io

JANUARY 2, 2024

But let’s be honest, creating effective, robust, and reliable data pipelines, the ones that feed your company’s reporting and analytics, is no walk in the park. From building the connectors to ensuring that data lands smoothly in your reporting warehouse, each step requires a nuanced understanding and strategic approach.

Data Pipeline 52

Data Pipeline Building Raw Data Data Warehouse 52

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

Cloudera

FEBRUARY 8, 2021

Below is the entire set of steps in the data lifecycle, and each step in the lifecycle will be supported by a dedicated blog post(see Fig. 1): Data Collection – data ingestion and monitoring at the edge (whether the edge be industrial sensors or people in a vehicle showroom). 2 ECC data enrichment pipeline.

Data Pipeline 118

Data Pipeline Building Manufacturing Data Warehouse 118

Netflix Tech

DECEMBER 21, 2020

By Anupom Syam Background At Netflix, our current data warehouse contains hundreds of Petabytes of data stored in AWS S3 , and each day we ingest and create additional Petabytes. Some of the optimizations are prerequisites for a high-performance data warehouse. More processing resources.

Data Warehouse 83

Data Warehouse Metadata Algorithm Data 83

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. Data ingestion When we think about the flow of data in a pipeline, data ingestion is where the data first enters our platform.

Data Pipeline 52

Data Pipeline Building Data Ingestion BI 52

Snowflake

OCTOBER 16, 2024

Many of our customers — from Marriott to AT&T — start their journey with the Snowflake AI Data Cloud by migrating their data warehousing workloads to the platform. Today we’re focusing on customers who migrated from a legacy data warehouse to Snowflake and some of the benefits they saw.

Digital Media 81

Digital Media Media Data Lake Data Warehouse 81

ProjectPro

DECEMBER 7, 2021

Data pipelines are a significant part of the big data domain, and every professional working or willing to work in this field must have extensive knowledge of them. Table of Contents What is a Data Pipeline? The Importance of a Data Pipeline What is an ETL Data Pipeline?

Data Pipeline 52

Data Pipeline Architecture Kafka AWS 52

Data Engineering Weekly

SEPTEMBER 18, 2024

The Rise of Data Observability Data observability has become increasingly critical as companies seek greater visibility into their data processes. This growing demand has found a natural synergy with the rise of the data lake. However, as with any advanced tool, data observability comes with costs and complexities.

Data Lake 98

Data Lake Data Pipeline Unstructured Data Datasets 98

Christophe Blefari

JANUARY 20, 2024

Data engineering inherits from years of data practices in US big companies. Hadoop initially led the way with Big Data and distributed computing on-premise to finally land on Modern Data Stack — in the cloud — with a data warehouse at the center. workflows (Airflow, Prefect, Dagster, etc.)

Data Engineer 130

Data Engineer Data Engineering Engineering Hadoop 130

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. These tools help organizations implement DataOps practices by providing a unified platform for data teams to collaborate, share, and manage their data assets.

Data Cleanse 52

Data Cleanse Data Pipeline Data Ingestion Data Validation 52

Data Engineering Podcast

NOVEMBER 20, 2022

Modern data teams are dealing with a lot of complexity in their data pipelines and analytical code. Monitoring data quality, tracing incidents, and testing changes can be daunting and often takes hours to days or even weeks. RudderStack helps you build a customer data platform on your warehouse or data lake.

Data Lake 130

Data Lake Data Ingestion MongoDB Google Cloud 130

phData: Data Engineering

MARCH 8, 2023

Examples of data sources and destinations include: Shopify Google Analytics Snowflake Data Cloud Oracle Salesforce Fivetran’s mission is to, “make access to data as easy as electricity” – so for the last 10 years, they have developed their platform into a leader in the cloud-based ELT market. What is Fivetran Used For?

IT 96

IT Data Warehouse Data Ingestion Data Integration 96

Data Engineering Weekly

JULY 7, 2024

Experience Enterprise-Grade Apache Airflow Astro augments Airflow with enterprise-grade features to enhance productivity, meet scalability and availability demands across your data pipelines, and more. Hudi seems to be a de facto choice for CDC data lake features. Notion migrated the insert heavy workload from Snowflake to Hudi.

Data Engineer 98

Data Engineer Data Engineering Engineering Data Lake 98

Data Engineering Weekly

MAY 16, 2023

I won’t bore you with the importance of data quality in the blog. Instead, Let’s examine the current data pipeline architecture and ask why data quality is expensive. Instead of looking at the implementation of the data quality frameworks, Let's examine the architectural patterns of the data pipeline.

Engineering 110

Engineering Kafka Data Pipeline Data Warehouse 110

DareData

JANUARY 30, 2023

In this post, we'll discuss some key data engineering concepts that data scientists should be familiar with, in order to be more effective in their roles. These concepts include concepts like data pipelines, data storage and retrieval, data orchestrators or infrastructure-as-code.

Data Engineer 52

Data Engineer Data Engineering NoSQL Engineering 52

Data Engineering Podcast

NOVEMBER 6, 2022

Their SDKs make event streaming from any app or website easy, and their extensive library of integrations enable you to automatically send data to hundreds of downstream tools. Sign up free at dataengineeringpodcast.com/rudder Modern data teams are dealing with a lot of complexity in their data pipelines and analytical code.

MongoDB 130

MongoDB Scala MySQL Machine Learning 130

Data Engineering Podcast

OCTOBER 30, 2022

Modern data teams are dealing with a lot of complexity in their data pipelines and analytical code. Monitoring data quality, tracing incidents, and testing changes can be daunting and often takes hours to days or even weeks. RudderStack helps you build a customer data platform on your warehouse or data lake.

Engineering 100

Engineering MongoDB Scala MySQL 100

Monte Carlo

MAY 26, 2023

Pro-tip : Don’t confuse data freshness with data latency. Data latency is the time between when the event occurs and when the data is available in the core data system (like a data warehouse) whereas data freshness is how recently the data within the final asset (table, BI report) has been updated.

Data Pipeline 98

Data Pipeline Data Data Warehouse Machine Learning 98

Cloudera

JANUARY 20, 2021

Most of what is written though has to do with the enabling technology platforms (cloud or edge or point solutions like data warehouses) or use cases that are driving these benefits (predictive analytics applied to preventive maintenance, financial institution’s fraud detection, or predictive health monitoring as examples) not the underlying data.

Manufacturing 109

Manufacturing Data Warehouse Kafka Retail 109

Analytics Vidhya

FEBRUARY 20, 2023

Introduction Azure data factory (ADF) is a cloud-based data ingestion and ETL (Extract, Transform, Load) tool. The data-driven workflow in ADF orchestrates and automates data movement and data transformation.

Data Ingestion 183

Data Ingestion Data Cloud Cloud Computing 183

Data Engineering Podcast

AUGUST 21, 2022

Data stacks are becoming more and more complex. This brings infinite possibilities for data pipelines to break and a host of other issues, severely deteriorating the quality of the data and causing teams to lose trust. Data stacks are becoming more and more complex. In fact, while only 3.5% In fact, while only 3.5%

Lambda Architecture 100

Lambda Architecture MongoDB Scala MySQL 100

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

APRIL 4, 2023

That’s why, in addition to integrating with your central data warehouse , lake , and lakehouse , Monte Carlo also integrates with transformation , orchestration , and now data ingestion tools. Fivetran automates the building and management of data pipelines, helping data teams scale.

BI 64

BI Data Ingestion Data Pipeline Metadata 64

Ascend.io

JUNE 8, 2023

The Essential Six Capabilities To set the stage for impactful and trustworthy data products in your organization, you need to invest in six foundational capabilities. Data pipelines Data integrity Data lineage Data stewardship Data catalog Data product costing Let’s review each one in detail.

Pipeline-centric 52

Pipeline-centric Database-centric Data Ingestion Data Pipeline 52

Data Engineering Podcast

AUGUST 13, 2022

Data stacks are becoming more and more complex. This brings infinite possibilities for data pipelines to break and a host of other issues, severely deteriorating the quality of the data and causing teams to lose trust. Data stacks are becoming more and more complex. In fact, while only 3.5% In fact, while only 3.5%

Metadata 100

Metadata MongoDB Scala MySQL 100

Data Engineering Podcast

NOVEMBER 13, 2022

Modern data teams are dealing with a lot of complexity in their data pipelines and analytical code. Monitoring data quality, tracing incidents, and testing changes can be daunting and often takes hours to days or even weeks. RudderStack helps you build a customer data platform on your warehouse or data lake.

MongoDB 100

MongoDB Google Cloud Scala MySQL 100

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

Data Engineering Podcast

AUGUST 28, 2022

Data stacks are becoming more and more complex. This brings infinite possibilities for data pipelines to break and a host of other issues, severely deteriorating the quality of the data and causing teams to lose trust. Data stacks are becoming more and more complex. In fact, while only 3.5% In fact, while only 3.5%

Building 100

Building MongoDB Scala MySQL 100

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 Engineer 52

Data Engineer Data Engineering Project Coding 52

Data Engineering Podcast

JULY 3, 2022

In order to quickly identify if and how two data systems are out of sync Gleb Mezhanskiy and Simon Eskildsen partnered to create the open source data-diff utility. Your newly mimicked datasets are safe to share with developers, QA, data scientists—heck, even distributed teams around the world. In fact, while only 3.5%

Data Integration 100

Data Integration MongoDB Scala MySQL 100

Data Engineering Weekly

OCTOBER 30, 2022

Data Engineering Weekly Is Brought to You by RudderStack RudderStack provides data pipelines that make it easy to collect data from every application, website, and SaaS platform, then activate it in your warehouse and business tools. Sign up free to test out the tool today.

Data Engineer 52

Data Engineer Data Engineering Engineering Data Ingestion 52

Data Engineering Podcast

JUNE 5, 2022

Modern data teams are dealing with a lot of complexity in their data pipelines and analytical code. Monitoring data quality, tracing incidents, and testing changes can be daunting and often takes hours to days or even weeks. Data teams are increasingly under pressure to deliver. In fact, while only 3.5%

Data Security 100

Data Security Metadata MongoDB Scala 100

Monte Carlo

JUNE 2, 2024

To get a better understanding of their responsibilities, we analyzed 41 job postings on LinkedIn and found these key duties: Designing and Implementing Data Pipelines: The primary responsibility in 90% of the job postings is creating and managing data pipelines.

Engineering 52

Engineering Amazon Web Services Data Science AWS 52

Monte Carlo

JUNE 2, 2024

To get a better understanding of their responsibilities, we analyzed 41 job postings on LinkedIn and found these key duties: Designing and Implementing Data Pipelines: The primary responsibility in 90% of the job postings is creating and managing data pipelines.

Engineering 52

Engineering Amazon Web Services Data Science AWS 52

Data Engineering Weekly

JUNE 4, 2023

Data Engineering Weekly Is Brought to You by RudderStack RudderStack provides data pipelines that make it easy to collect data from every application, website, and SaaS platform, then activate it in your warehouse and business tools. Perhaps unit test the pipeline? Sign up free to test out the tool today.

Data Engineer 52

Data Engineer Data Engineering Engineering Medical 52

Data Engineering Podcast

AUGUST 13, 2022

Data stacks are becoming more and more complex. This brings infinite possibilities for data pipelines to break and a host of other issues, severely deteriorating the quality of the data and causing teams to lose trust. Data stacks are becoming more and more complex. In fact, while only 3.5% In fact, while only 3.5%

Machine Learning 100

Machine Learning Pipeline-centric Database-centric MongoDB 100