What Makes Data "AI Ready"?
https://youtu.be/VbblUXqtkhc

#theDataChannel #artificalintelligence #ai

Module 2-Data Preparation and Feature Engineering- Data imputation
https://youtu.be/Ip3EPqxi79k

#theDataChannel #databricks #dataengineer #machinelearning

Module 1-Data Preparation for Machine Learning: Load and Explore Data- DEMO
https://youtu.be/g7tCRuNdmUI

#theDataChannel #machineLearning #databricks

Module 1-Data Preparation for Machine Learning: Managing and Exploring Data
https://youtu.be/EDm07dSQVGI

#Databricks #theDataChannel #dataengineering #machinelearning

Data Engineering Tip Day 26: Consolidate Pipeline Failures

🔹 Tip: Centralize failure notifications and logs across all pipelines using a single dashboard or alerting system.

🔸 Why?: Jumping across tools wastes time. Use Slack/Teams alerts, PagerDuty, or custom dashboards with tools like Grafana + Loki.

#theDataChannel #dataengineering

Data Engineering Tip Day 25: Validate Business Logic with Stakeholders

🔹 Tip: Collaborate with business users to validate data transformations and KPIs early in development.

🔸 Why?: Technical correctness ≠ business correctness. Avoid the trap of “technically correct, practically useless.”

#theDataChannel #dataengineering

Data Engineering Tip Day 24: Secure Your Pipelines End-to-End

🔹 Tip: Encrypt data at rest and in transit. Use role-based access control (RBAC) and secrets managers.

🔸 Why?: Data pipelines often handle sensitive data. Use Vault, AWS KMS, Azure Key Vault, or Databricks Secrets.

#theDataChannel #Databricks #dataengineering

Dear members, after overwhelming response to Databricks Associate- Spark and Databricks Data Engineer Associate, we are excited to introduce very relevant and most demanded and one of it’s kind Certification course for - “Databricks Machine Learning Engineer- Associate”

Hope you all enjoy this course and get use of it, Thank you

Databricks Certified Machine Learning- Associate: #1 Introduction
https://youtu.be/YbFzqX1eTiw

#theDataChannel #databricks #machinelearning

Data Engineering Tip Day 23: Avoid Overuse of UDFs

🔹 Tip: Use built-in functions in Spark/SQL instead of custom UDFs whenever possible.

🔸 Why?: UDFs break query optimization, slow down performance, and add serialization overhead. Use them only when absolutely needed.

#dataEngineering #theDataChannel

Data Engineering tip Day 22: Automate Metadata Collection

🔹 Tip: Automatically capture and store metadata (e.g., column types, null percentages, row counts) as part of your pipeline.

🔸 Why?: Metadata enables better governance, profiling, and debugging. Tools: OpenMetadata, Amundsen, DataHub, Databricks Unity Catalog.
#theDataChannel #dataengineering