AltexSoft

OCTOBER 21, 2022

After trying all options existing on the market — from messaging systems to ETL tools — in-house data engineers decided to design a totally new solution for metrics monitoring and user activity tracking which would handle billions of messages a day. cloud data warehouses — for example, Snowflake , Google BigQuery, and Amazon Redshift.

Kafka 93

Kafka Hadoop Big Data ETL Tools 93

Ascend.io

AUGUST 31, 2023

These requirements are typically met by ETL tools, like Informatica, that include their own transform engines to “do the work” of cleaning, normalizing, and integrating the data as it is loaded into the data warehouse schema. Orchestration tools like Airflow are required to manage the flow across tools.

Data Lake 52

Data Lake Data Warehouse ETL Tools Data Pipeline 52

ProjectPro

NOVEMBER 30, 2021

Cloud: Technology advancements, information security threats, faster internet speeds, and a push to prevent data loss have contributed to the move toward cloud-native storage and processing. The AWS Glue Data Catalog automatically loads your data and the associated metadata.

Process 52

Process Data Warehouse Data Pipeline AWS 52

dbt Developer Hub

NOVEMBER 28, 2021

From the Airflow side ​ A client has 100 data pipelines running via a cron job in a GCP (Google Cloud Platform) virtual machine, every day at 8am. In a Google Cloud Storage bucket. It was simple to set up, but then the conversation started flowing: “Where am I going to put logs?” Where can I view history in a table format?”

SQL 52

SQL Google Cloud Cloud Consulting 52

phData: Data Engineering

SEPTEMBER 27, 2024

But with modern cloud storage solutions and clever techniques like log compaction (where obsolete entries are removed), this is becoming less and less of an issue. The benefits of log-based approaches often far outweigh the storage costs. But with the right tools and processes, these challenges are manageable.

Data 52

Data Raw Data Data Lake Architecture 52

Towards Data Science

NOVEMBER 4, 2023

") Apache Airflow , for example, is not an ETL tool per se but it helps to organize our ETL pipelines into a nice visualization of dependency graphs (DAGs) to describe the relationships between tasks. Typical Airflow architecture includes a schduler based on metadata, executors, workers and tasks. Image by author.

Data Engineer 98

Data Engineer Data Engineering Engineering BI 98

Edureka

JULY 3, 2024

Publish: Transformed data is then published either back to on-premises sources like SQL Server or kept in cloud storage. This makes the data ready for consumption by BI tools, analytics applications, or other systems. It’s like a central hub that orchestrates how your data flows across your cloud environment.

Pipeline-centric 52

Pipeline-centric Data Lake Database-centric Data Pipeline 52