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Data Warehouse Migration Story for FinTrust Bank

Framework Step	Details
Situation	FinTrust Bank, with an annual revenue of $12 billion, was facing a high-stakes challenge when its existing systems couldn't handle over 500 million transactions per day during a critical testing phase with a key e-commerce client. This client was projected to increase annual revenues by 15% ($1.8 billion). Key stakeholders involved were the client's CIO, CTO, and CSO, highlighting the strategic importance of the project.
Task	The urgent task was to stabilize and scale the bank’s data processing capabilities to not only retain the e-commerce client but also to set a foundation for scalable, compliant growth suitable for high-volume transaction environments.

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1. Data processing frameworks
2. Batch and real-time streaming analytics
3. SQL versus NoSQL use cases and use case patterns
4. Enterprise data governance and metadata management

CI/CD DataOps

Category	Tools

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Here's the table sorted chronologically based on the release date of each Google Cloud service:

Google Cloud Service	Release Date	Based on/Open-source Inspiration	Open-source Start Date	Notes
Google BigQuery	2010	Dremel (Internal Google Tech)	N/A	BigQuery is inspired by Dremel but is not directly based on open-source technology.
Google Cloud Dataflow	2014	Apache Beam	2016 (as Apache Beam)	Initially developed by Google as Google Dataflow, then donated to the Apache Software Foundation as Apache Beam.
Google Cloud Composer	2018	Apache Airflow	2015	Developed by Airbnb and later open-sourced as Apache Airflow, which Google adopted for Cloud Composer.
Google Data Fusion	2019	CDAP (Cask Data Application Platform)	2011

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Fundamentals ofData Engineering

https://drive.google.com/file/d/15iRNG9Pg4iz9ulwghLFAtPhVVBvRQCuV/view?usp=drive_link

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Techniques to handle late arrival

Watermarks and Allowed Lateness are both vital techniques in managing late data in stream processing systems, but they serve slightly different purposes and are often used in conjunction to maximize data integrity and processing efficiency. Here’s an in-depth look at when and why you might choose to use each technique, or both together, along with real-world industry examples.

Watermarks

Purpose: Watermarks are primarily used to handle out-of-order data. They provide a way to estimate the "completeness" of data up to a certain point in time, based on event timestamps.

When to Use: Use watermarks when:

• You expect data to arrive out of order.
• You need a mechanism to know when to close a window and process its data.

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• GCP PDF 1: Big Data and Machine Learning on Google Cloud

• GCP PDF 2: Data Engineering with Streaming Data

• Apache Data Processing Frameworks

• [Apache Spark Notes](https:

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Kubernetes from Basics to Guru

Kubernetes: From Basics to Guru

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Apache Spark Learning Journey

This document outlines the structured content of my learning journey through Apache Spark, covering various topics from installation to advanced data processing techniques.

Course name: Spark Programming in Python for Beginners with Apache Spark 3

• Chapter 1: Apache Spark Introduction
• Chapter 2: Installing and Using Apache Spark
• Chapter 3: Spark Execution Model and Architecture

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Comprehensive Overview of Hadoop Ecosystem Components with Cloud Service Equivalents

Here's a concise table summarizing the key Hadoop ecosystem components along with their cloud service equivalents:

Component	Purpose	Created by	Language Support	Limitations	Alternatives	Fit	GCP Service	AWS Service	Azure Service
Apache Hive	SQL-like data querying in Hadoop.	Facebook	HiveQL	High latency for some queries.	P

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Here's an updated and detailed table that categorizes the open-source projects related to Google's technologies, including those inspired by, created by, or driven by Google. I've added a category column to better classify each project based on its primary function or architecture:

Open Source Project	Description	Inspired by Google Technology	Category
Apache Hadoop	A framework for distributed storage and processing of large data sets on computer clusters using simple programming models.	Google's MapReduce and GFS (Google File System).	Distributed Processing
Apache Cassandra	A distributed NoSQL database designed to handle large amounts of data across many commodity servers.	Google's Bigtable, a distributed storage system for managing structured data.	NoSQL Database
Apache Beam	A unified programming model for defining and executing data processing pipelines, including ETL, batch, and s