Wednesday, September 17, 2025

Capital Markets & Investment Management services and Trade Lifecycle and Investment Data Flow

Here’s a structured way you can describe your domain experience in Capital Markets & Investment Management services during an interview, especially if the role is data/engineering focused:

1. Start with a high-level domain understanding

“I have experience working in the Capital Markets and Investment Management domain, where I’ve gained strong knowledge of financial products, market structures, and investment workflows.”

Mention:

Capital Markets – Equities, Fixed Income, Derivatives, Trading life cycle (order, execution, settlement, clearing).
Investment Management – Portfolio management, Asset allocation, Risk & performance measurement, Regulatory reporting.

2. Connect business processes to data/technology

“Most of my work has been at the intersection of business processes and technology. For example, I’ve worked with trade and position data, reference data (securities, pricing, corporate actions), and market data integration. On the investment side, I have experience with portfolio holdings, fund performance, NAV calculations, and client reporting.”

3. Show awareness of key challenges in the domain

Data challenges: large transaction volumes, high data quality requirements, real-time feeds, golden copy/reference data.
Regulatory requirements: MiFID II, Basel, FATCA, SEBI/SEC compliance.
Operational processes: front-office trading, middle-office risk and compliance, back-office settlement.

4. Tie to your role (data engineering / analytics / ETL / reporting)

“In my projects, I built data pipelines for trade capture and settlement, developed data models for positions & holdings, and created reporting solutions for portfolio performance and regulatory compliance. I also worked with stakeholders like traders, portfolio managers, and risk analysts to understand their requirements and translate them into technical solutions.”

5. Give an example (STAR method)

Situation: Investment firm needed consolidated view of client portfolios.
Task: Build a data warehouse integrating trades, positions, and market data.
Action: Designed star schema for portfolio analysis, built ETL pipelines, automated NAV & performance reports.
Result: Reduced reporting time from 2 days to a few hours, improving decision-making for portfolio managers.

👉 This way, you’re showing domain knowledge + technical alignment + business impact, which interviewers in capital markets/data engineering roles look for.

Do you want me to prepare a ready-to-use 2–3 minute interview pitch combining all the above into a smooth answer?

yes

Perfect 👍 — here’s a ready-to-use 2–3 minute interview pitch you can deliver when asked about your Capital Markets & Investment Management domain experience:

Interview Pitch

“I have strong experience in the Capital Markets and Investment Management domain, particularly around trade, portfolio, and market data.

On the capital markets side, I’ve worked with different asset classes like equities, fixed income, and derivatives, and I understand the full trade life cycle — from order capture and execution to settlement and clearing. I’ve also handled reference data such as securities, pricing, and corporate actions, which are critical for maintaining accurate downstream reporting.

On the investment management side, my experience covers portfolio holdings, asset allocation, NAV calculations, and fund performance reporting. I’ve been involved in building data solutions that support portfolio managers and risk teams with timely and accurate insights.

From a data engineering perspective, I designed and built ETL pipelines to integrate trade and position data with market and reference data. I also developed data models — for example, star schemas for portfolio and performance analysis — and automated regulatory and client reporting. One key project was creating a consolidated data warehouse for an investment firm, where I integrated trades, holdings, and market data. This solution reduced reporting turnaround time from two days to just a few hours, enabling portfolio managers to make faster investment decisions.

Along the way, I’ve also worked closely with traders, portfolio managers, compliance, and risk teams to ensure the solutions align with business and regulatory requirements. This combination of domain knowledge and technical skills helps me bridge the gap between business needs and data solutions.”

That’s a crisp, 2–3 minute summary that shows:
✅ Domain knowledge (products, processes)
✅ Technical expertise (data modeling, ETL, reporting)
✅ Business impact (efficiency, compliance, decision-making)

Would you like me to also create a shorter 30-second version (for quick self-intro) along with this detailed 2–3 minute pitch?

yes

Great — here’s a 30-second quick self-intro version of your Capital Markets & Investment Management domain experience:

30-Second Intro

“I have experience in the Capital Markets and Investment Management domain, working across the trade life cycle, portfolio management, and regulatory reporting. From a data engineering perspective, I’ve built ETL pipelines, designed data models for trades and portfolios, and automated reporting solutions. For example, I developed a consolidated data warehouse that integrated trade, position, and market data, which reduced reporting time from two days to just a few hours and supported faster investment decisions.”

👉 Use this short one for quick introductions or follow-ups when time is limited, and the 2–3 minute pitch when the interviewer asks you to elaborate.

Would you like me to also prepare a bullet-point version you can quickly glance at before the interview to keep both answers fresh in your mind?

describe the Understanding of trade lifecycle and flow of Investment data

Got it 👍 — here’s how you can describe your understanding of the trade lifecycle and the flow of investment data in an interview:

1. Trade Lifecycle (End-to-End Flow)

You can explain step by step:

Order Initiation –
- A trader or portfolio manager places an order (buy/sell).
- Captured in Order Management Systems (OMS).
Order Execution –
- Orders are routed to exchanges, brokers, or trading platforms.
- Executions are confirmed and matched.
Trade Capture & Enrichment –
- Trade details (instrument, price, quantity, counterparty) are stored in Trade Capture Systems.
- Enrichment adds reference data (security identifiers, settlement instructions).
Trade Validation & Confirmation –
- Middle office validates the trade, confirms with counterparty (via FIX, SWIFT, or electronic confirmations).
Clearing & Netting –
- Trades are sent to clearing houses for risk mitigation.
- Netting reduces the number of settlement obligations.
Settlement –
- Actual exchange of cash and securities (e.g., T+1/T+2).
- Settlement failures are monitored and resolved.
Post-Settlement & Reporting –
- Trades flow into downstream systems: accounting, risk, P&L, performance, regulatory reporting.

2. Flow of Investment Data

Data moves through multiple layers:

Front Office Data (Trading & Portfolio Management)
- Orders, executions, real-time market data, positions.
Middle Office Data (Risk & Compliance)
- Trade validations, exposure, limits, regulatory checks.
Back Office Data (Settlement & Accounting)
- Cash flows, reconciliations, corporate actions, NAV calculations.
Reference & Market Data (used throughout)
- Security master, pricing feeds (Bloomberg, Reuters), benchmarks, ratings.
Reporting & Analytics
- Client reporting (portfolio performance, holdings).
- Regulatory reporting (MiFID, FATCA, Basel, SEBI/SEC).

3. How to Explain in Interview (Sample Answer)

“I have a clear understanding of the trade lifecycle, starting from order initiation in the front office to execution, confirmation, clearing, and final settlement in the back office. Each stage generates critical data — for example, trades are enriched with security reference data, confirmed with counterparties, and then flow downstream for settlement, accounting, and regulatory reporting.

On the investment data side, I understand how different data types interact: market data, security master data, trades, positions, and corporate actions. These feed into portfolio management systems for NAV and performance calculation, as well as compliance and client reporting. This end-to-end view helps me design data pipelines and models that ensure data accuracy, timeliness, and regulatory compliance.”

Would you like me to also draw a simple diagram-style explanation of the Trade Lifecycle & Data Flow (so you can visualize it quickly before your interview)?

yes

Here’s a visual diagram of the Trade Lifecycle & Investment Data Flow you can use to quickly recall the stages before your interview:

Starts with Order Initiation (Trader/PM)
Moves through Execution, Trade Capture, and Validation
Then goes to Clearing & Netting, Settlement
Finally ends with Post-Settlement Reporting (Risk, P&L, Regulatory)

Would you like me to also make a second diagram that shows how investment data flows across Front Office → Middle Office → Back Office → Reporting?

yes

Here’s the Flow of Investment Data diagram:

Front Office → Trading & Portfolio Mgmt (orders, executions, positions)
Middle Office → Risk & Compliance (validations, exposure, limits)
Back Office → Settlement & Accounting (cash flows, reconciliations, NAV)
Reporting & Analytics → Client & Regulatory reporting (performance, holdings, MiFID, FATCA)
Reference & Market Data (security master, prices, benchmarks) supports all stages.

Would you like me to also prepare a short verbal explanation you can memorize and say in the interview while pointing to such a flow?

yes

Here’s a short verbal explanation you can use in the interview while visualizing or pointing to the flow diagram:

Answer Example – Flow of Investment Data

“Investment data typically flows through the front, middle, and back offices, with reference and market data supporting all stages.

In the front office, data is generated from trading and portfolio management — like orders, executions, and positions.

This flows into the middle office, where risk and compliance teams validate trades, monitor exposures, and ensure regulatory limits are met.

Then it moves to the back office, where settlement, reconciliations, cash flows, and NAV calculations are handled.

Finally, all this data is consolidated for reporting and analytics — both client reporting, such as portfolio performance and holdings, and regulatory reporting like MiFID or FATCA.

Throughout the process, reference and market data — such as security master, prices, benchmarks, and ratings — enriches and validates the transactions at each stage.”

👉 This version is under 2 minutes, structured, and shows you clearly understand the data flow from both a business and data perspective.

Do you want me to also create a crisp 3–4 line version of this (for quick questions where they want just the essence)?

yes

Here’s the crisp 3–4 line version you can use when asked briefly about investment data flow:

Quick Version

“Investment data flows from the front office with trades and positions, to the middle office for risk and compliance checks, and then to the back office for settlement, reconciliations, and NAV. Finally, it’s consolidated for client and regulatory reporting, with reference and market data enriching every stage.”

Would you like me to now prepare a combined cheat sheet (trade lifecycle + investment data flow, both long & short versions) so you can revise quickly before your interview?

yes

Perfect 👍 Here’s your Capital Markets & Investment Management Cheat Sheet — covering Trade Lifecycle and Investment Data Flow with both detailed and short versions.

📌 Capital Markets & Investment Management – Interview Cheat Sheet

1. Trade Lifecycle (Detailed)

Order Initiation – Trader/PM places buy/sell order (OMS).
Order Execution – Routed to broker/exchange, executed.
Trade Capture & Enrichment – Trade booked, enriched with reference data.
Validation & Confirmation – Middle office validates, confirms with counterparty.
Clearing & Netting – Trades sent to clearing house, net obligations.
Settlement – Cash/securities exchanged (T+1/T+2).
Post-Settlement & Reporting – Risk, P&L, accounting, regulatory reporting.

✅ Short Version

“The trade lifecycle runs from order placement and execution, through capture, validation, clearing, and settlement, and finally into reporting and accounting.”

2. Investment Data Flow (Detailed)

Front Office → Orders, executions, positions, portfolio management.
Middle Office → Trade validations, risk checks, compliance limits.
Back Office → Settlement, reconciliations, NAV, accounting.
Reporting & Analytics → Client reports (performance/holdings), regulatory (MiFID, FATCA).
Reference & Market Data → Security master, pricing feeds, benchmarks — enriches every stage.

✅ Short Version

“Investment data flows from the front office (trades/positions), to the middle office (risk & compliance), then to the back office (settlement & NAV), and finally into reporting. Reference and market data enrich all stages.”

3. Combined 30-Second Pitch

“I have strong understanding of the trade lifecycle and investment data flow. Trades move from order initiation and execution, through capture, validation, clearing, and settlement, before flowing into downstream reporting. Similarly, investment data travels across front, middle, and back office functions, with reference and market data enriching every stage, ultimately supporting portfolio management, client reporting, and regulatory compliance.”

👉 This cheat sheet gives you both in-depth detail (for deep questions) and crisp summaries (for quick answers).

Would you like me to also create a story-style example (STAR method) combining these flows, so you can give a real-world style answer in the interview?

Saturday, August 16, 2025

Python using AI

Python using AI - Prompts & Codes

Tools useful for Python + AI
1. ChatGPT - https://chatgpt.com/
2. Claude AI - https://claude.ai/new
3. Gemini - https://gemini.google.com/app
4. Co-pilot - https://copilot.microsoft.com/chats/VF3SLiaBL2NTuEYKGdKRS
5. Perplexity - https://www.perplexity.ai/
6. Cursor - https://www.cursor.com/
7. W3Schools - https://www.w3schools.com/
8. Leetcode - https://leetcode.com/
9. HackerRank - https://www.hackerrank.com/
10. Colab - https://colab.research.google.com/
11. Kaggle - https://www.kaggle.com/

Steps to open Colab

Search for “Colab” → Open the first link
File → New Notebook

Prompt 1

Assume i'm a complete beginner in Python, who has never written a code, tell a simple line to run

print("Hello, World!")

Prompt 2

I have 10 days of holidays, and I want to use these 10 days to learn Python, and I have 2 hours of free time every day, now prepare daily and hourly planner so that I'm at least good with Python basics

Prompt 3

Please give me a list of 10 MCQs on Python basics, make the options very tricky to choose

Prompt 4 Now give me 30 more of these and make the level to medium

Prompt 5

Can you explain me about this error: SyntaxError: incomplete input

Prompt 6

can you fix this code <paste your code>

Error code

wallet_amount = 100
ticket_price = 120

if wallet_amount > ticket_price:
  print("Go for a movie")
else:
  print("Go home"

Corrected code

wallet_amount = 100
ticket_price = 120

if wallet_amount > ticket_price:
  print("Go for a movie")
else:
  print("Go home")

Prompt 7 I started learning IF ELSE in Python, now help me understand about this

concept from scratch, with some examples in real life, assume you are

explaining this to a 5-year-old kid

toy_color = input("What color is the toy? ")

if toy_color == "red":
    print("Yay! I found a red toy!")
else:
    print("Oh no! No red toys here.")

Prompt 8

I want to create a world map visual in Python, if I hover over a country it should display that country name

import plotly.graph_objects as go
import pandas as pd

# Load the built-in dataset of country names and codes
df = pd.read_csv('<https://raw.githubusercontent.com/plotly/datasets/master/2014_world_gdp_with_codes.csv>')

# Create the choropleth map
fig = go.Figure(data=go.Choropleth(
    locations = df['CODE'],
    z = df['GDP (BILLIONS)'],
    text = df['COUNTRY'],
    colorscale = 'Blues',
    autocolorscale=False,
    reversescale=True,
    marker_line_color='darkgray',
    marker_line_width=0.5,
    colorbar_title = 'GDP Billions USD',
))

# Update the layout
fig.update_layout(
    title_text='World Map',
    geo=dict(
        showframe=False,
        showcoastlines=True,
        projection_type='equirectangular'
    ),
    annotations = [dict(
        x=0.55,
        y=0.1,
        xref='paper',
        yref='paper',
        text='Source: <a href="<https://www.cia.gov/library/publications/the-world-factbook/fields/2195.html>">\\
            CIA World Factbook</a>',
        showarrow = False
    )]
)

# Show the figure
fig.show()

How to set up Visual Studio Code?

Download “Visual Studio Code” - Latest version for Windows / Mac / Linux
Download “Python” - Latest version for Windows / Mac / Linux
Create a new file “app.py”
Paste your AI generated code below into this “app.py” file
Run this command streamlit run app.py
In case you get any errors → please run this command → pip install streamlit

Prompt 9 can you help me build a website in Python using Streamlit, it has to do the following: it should be a calculator with basic add, minus, multiply, and divide, it should have buttons to click as well

import streamlit as st

def calculate(num1, num2, operation):
    if operation == '+':
        return num1 + num2
    elif operation == '-':
        return num1 - num2
    elif operation == '*':
        return num1 * num2
    elif operation == '/':
        return num1 / num2 if num2 != 0 else "Error: Division by zero"

st.title("Simple Calculator")

# Input fields
num1 = st.number_input("Enter first number", value=0.0)
num2 = st.number_input("Enter second number", value=0.0)

# Operation buttons with Unicode symbols
col1, col2, col3, col4 = st.columns(4)
with col1:
    if st.button("\\u2795"):  # Heavy Plus Sign
        result = calculate(num1, num2, '+')
with col2:
    if st.button("\\u2796"):  # Heavy Minus Sign
        result = calculate(num1, num2, '-')
with col3:
    if st.button("\\u2716"):  # Multiplication X
        result = calculate(num1, num2, '*')
with col4:
    if st.button("\\u2797"):  # Heavy Division Sign
        result = calculate(num1, num2, '/')

# Display result
if 'result' in locals():
    st.success(f"Result: {result}")

# Add a note about the symbols
st.write("Button symbols: ➕ (add), ➖ (subtract), ✖️ (multiply), ➗ (divide)")

Prompt 10

Write a Python script to scrape static content from a website. Use the URL http://books.toscrape.com/ to scrape book titles and their prices. Parse the webpage using BeautifulSoup and print the data in a clean, readable format. Ensure the script works for static, non-time-sensitive pages, and include error handling for HTTP requests. Additionally, provide an option to save the scraped data to a CSV file.

import requests
from bs4 import BeautifulSoup

# URL of the website to scrape
url = "<http://books.toscrape.com/>"

# Send an HTTP GET request to the URL
response = requests.get(url)

# Parse the page content using BeautifulSoup
soup = BeautifulSoup(response.content, 'html.parser')

# Find all book containers
books = soup.find_all('article', class_='product_pod')

# List to store scraped data
book_data = []

# Loop through each book container to extract details
for book in books:
    # Extract book title
    title = book.h3.a['title']
    
    # Extract book price
    price = book.find('p', class_='price_color').text
    
    # Add details to the list
    book_data.append({'title': title, 'price': price})

# Display the scraped data
for i, book in enumerate(book_data, start=1):
    print(f"{i}. {book['title']} - {book['price']}")

import csv

# Save the data to a CSV file
with open('books.csv', 'w', newline='', encoding='utf-8') as csvfile:
    writer = csv.DictWriter(csvfile, fieldnames=['title', 'price'])
    writer.writeheader()
    writer.writerows(book_data)
print("Data saved to books.csv")

Prompt 11

can you help me scrape the T20I overall runs from this URL:

https://www.cricbuzz.com/profiles/1413/virat-kohli

import requests
from bs4 import BeautifulSoup

def scrape_t20i_runs(url):
    # Send a GET request to the URL
    response = requests.get(url)
    
    # Check if the request was successful
    if response.status_code == 200:
        # Parse the HTML content
        soup = BeautifulSoup(response.text, 'html.parser')
        
        # Find the table containing the player stats
        table = soup.find('table', class_='table cb-col-100 cb-plyr-thead')
        
        if table:
            # Find all rows in the table
            rows = table.find_all('tr')
            
            # Look for the T20I row
            for row in rows:
                cols = row.find_all('td')
                if cols and cols[0].text.strip() == 'T20I':
                    # The runs are in the third column (index 2)
                    t20i_runs = cols[4].text.strip()
                    return t20i_runs
        
        print("Couldn't find T20I stats in the table.")
        return None
    else:
        print(f"Failed to retrieve the webpage. Status code: {response.status_code}")
        return None

# URL of Virat Kohli's profile
url = "<https://www.cricbuzz.com/profiles/1413/virat-kohli>"

# Scrape T20I runs
t20i_runs = scrape_t20i_runs(url)

if t20i_runs:
    print(f"Virat Kohli's T20I overall runs: {t20i_runs}")
else:
    print("Failed to scrape T20I runs.")

Prompt 11

Decode the entire content of the page

Prompt 12

https://bolt.new/

https://www.cursor.com/

Create a python app to manage my to do list

Prompt 13

can you build a tic tac toe in python using flask

Prompt 14

can you build a todo app in python using flask

How to Top 1% people learn Python?

Leetcode
HackerRank
Participating in Coding contests
While preparing for college interviews
While preparation for job interviews
Use Blogs (GeeksforGeeks, Stackoverflow, Github forums, …)
Use Youtube Channels (TheNewBoston, …)

How to Top 1% people use LLMs?

Use one LLM for everything at the start
Slowly figure out which LLM works best for them for various use cases
Continuous learning
Additional Links

Additional Code

Code 1

l = [1, 2, 2, 2, 3, 4]

# unique elements
a1 = []

for i in l:
  if i not in a1:
    a1.append(i)

# frequencies
a2 = []
for i in a1:
  count = 0
  for j in l:
    if i == j:
      count += 1
  a2.append((i, count))

Code 2

l = [1, 2, 2, 2, 3, 4]
d = {}

for i in l:
  if i not in d:
    d[i] = 1
  else:
    d[i] += 1

Monday, June 23, 2025

Streaming data design

Can you explain how you would design a data pipeline to handle real-time streaming data for a retail application? What tools and technologies would you use, and why?

Certainly! Designing a 'Streaming data pipeline' for a retail application involves several stages — from ingesting streaming data (like sales, inventory, user activity) to processing, storing, and visualizing it in near real-time.

High-Level Architecture

   Data Sources (POS, Web, Mobile) 
        ↓
   Streaming Ingestion (Kafka / Kinesis)
        ↓
   Stream Processing (Flink / Spark Structured Streaming)
        ↓
   Real-time Data Store (Apache Hudi / Delta Lake / BigQuery)
        ↓
   BI Dashboard / Alerting (Power BI / Grafana / Looker)

[Data Sources] → 
[Ingestion Layer] → 
[Processing Layer] → 
[Storage Layer] → 
[Serving Layer] → 
[Analytics/Apps]


A retail chain wants to monitor inventory and sales in real time. 
Kafka collects transactions from store POS systems. 
Spark Streaming processes the data to compute hourly sales and stock levels. 
Processed data is written to Delta Lake on S3, and a Power BI dashboard shows regional sales trends and low-stock alerts updated every minute.

Snowflake QA

Snowflake Interview Questions and Answers

1. What Is Snowflake?

Snowflake is a cloud-based data warehousing service that was built specifically for the cloud. It allows organizations to store and analyze large amounts of data efficiently by leveraging the elasticity, scalability, and performance of the cloud. Key features of Snowflake include separation of storage and computing, support for both structured and semi-structured data, data sharing capabilities, and near-zero maintenance.

1. What Are Snowflake Databases, Warehouses, and Stages?

In Snowflake, a database is a logical unit to organize objects like tables, views, etc. A warehouse is a virtual data warehouse built on cloud infrastructure. It provides the computing resources needed to process and analyze the data stored in Snowflake. A stage is an intermediate storage area used for loading data into and unloading data out of Snowflake. Stages allow you to process the data before loading it into a Snowflake table.

1. Explain Snowflake Architecture.

The Snowflake architecture consists of 3 independent layers - Storage, Compute, and Cloud Services. The storage layer decouples storage from computing, allowing them to scale independently. The compute layer consists of virtual warehouses that provide the processing power. The cloud services layer has services like metadata, security, access control, etc. A central repository stores all metadata. This architecture provides flexibility, scalability, and high concurrency while separating storage and computing.

1. What Are the Benefits of Using Snowflake?

Some key benefits of Snowflake include:

· Near zero maintenance and tuning needed

· Built for the cloud and can leverage cloud economies of scale

· Flexible scaling of storage and compute

· Faster query processing with caching and query optimization

· Data sharing capability for easy access control and governance

· Support for semi-structured and structured data

· Secure data sharing across accounts and cloud platforms

1. How Is Data Loaded Into Snowflake?

There are several ways to load data into Snowflake:

· Using the Snowflake user interface

· Using the Snowflake CLI (command line interface)

· Using the Snowflake APIs to load data from an application

· Using a Snowflake connector like the JDBC or ODBC driver

· Using a cloud service like Amazon S3

· Using a tool like Informatica or Talend for ETL processes

Stages and tables need to be created before loading data. Copy commands are used to load data into stages/tables from files or external sources.

1. Explain Snowflake Table Clustering.

Table clustering allows you to cluster data in a table based on one or more columns. It stores related data together instead of in random order. This leads to faster query performance as related data is co-located and requires less scanning. Some key points:

· Automatic and transparent to users

· Performed during loading and maintenance operations

· Clustering keys determined automatically or specified manually

· Queries automatically leverage clustering without any changes needed

1. What Are Snowflake Time Travel and Zero-Copy Clone Capabilities?

Snowflake time travel allows querying a table at any point in the past (for up to 90 days) without the need for restoring backups or DB snapshots. Zero-copy cloning quickly creates a new table by creating metadata pointing to the same data as the original table. Both these capabilities use Snowflake's internal metadata and storage architecture to provide easy access to historical data and clones without duplicating data.

1. What Is a Snowflake Secure Data Sharing?

Snowflake's secure data sharing allows data in Snowflake to be securely shared across accounts, roles, warehouses, databases, schemas, and even different organizations seamlessly. Data does not need to be replicated, copied, or moved. Consumers access a shared view of the data with permissions and row-level security policies applied automatically. This facilitates easy, governed data access.

1. What Are Snowflake’s Roles and Why Are They Important?

Snowflake has a role-based access control (RBAC) model to enable secure access and data protection. Some key aspects are:

· Roles centralize access control

· Roles can be assigned privileges like creating/accessing tables, operating warehouses

· Roles can have other roles granted to them

· Roles allow the grouping of privileges and inheritance of privileges

· Roles enable multi-factor authentication for security

Proper role setup is crucial for access control and security.

1. How Does Snowflake Provide Security?

Some key security features of Snowflake are:

· Role-based access control for access management

· Column-level encryption for protected data

· Masking policies to mask sensitive data

· Data redaction for displaying portions of data

· Client-side and server-side field-level encryption

· Key pair authentication for additional authentication

· Secure data sharing model with permissions

· Compliance with standards like HIPAA, GDPR, etc.

1. What Are Snowflake Tasks?

Snowflake tasks allow you to run a piece of code/script asynchronously on Snowflake. They are executed in a separate transaction and context from the caller. Key aspects:

· Used for executing JavaScript, SQL, etc

· Help with ETL workflows, maintenance operations

· Can chain tasks together using dependencies

· Status and results can be checked programmatically

· Can configure auto-retries, notifications on failure

· Useful for long-running operations, parallel execution

1. Explain Snowflake Streams and Tasks.

Snowflake Streams capture changes to tables and provide change data to consumers in near real-time. Tasks help run async pieces of code like ETL transformations.

Key differences:

· Streams capture changes, Tasks run code

· Streams continuously, Tasks run once

· Streams read-only, Tasks can transform data

· Streams require setup for tables, Tasks can run ad hoc

· Streams data capture, Tasks general purpose execution

They can be used together for capturing changes and processing them.

1. What Are Snowflake Micro-Partitions?

Snowflake uses a unique architecture to store table data in columnar format across a large number of micro-partitions. Key aspects:

· Small units of table storage automatically managed by Snowflake

· New micro-partitions are added as data grows

· Micro-partitions transparently pruned during query execution

· Columnar storage provides performance benefits

· Parallel queries can process multiple micro-partitions

· Provides scalability and query concurrency

1. Explain Snowflake Caching.

Snowflake employs multiple caching techniques to improve performance:

· Query Result Caching - Caches results of queries

· Data Caching - Caches hot data for faster reads

· Materialized Views - Reuse query results without re-running queries

· Clones - Quickly create replicas of tables/databases

Caching is automatic and managed transparently by Snowflake to optimize performance. Caching time and other policies can also be configured.

1. What Are Snowflake Virtual Warehouses?

Snowflake virtual warehouses provide the compute resources required for processing queries on the data stored in Snowflake. Key points:

· Fully managed and sized based on cores, memory, warehouses

· Auto-suspend when not in use to save costs

· Scale up or down on demand for varying workloads

· Pay only for the time warehouses are running

· Concurrency achieved by multiple virtual warehouses

1. How Does Snowflake Handle Semi-structured Data?

Snowflake has first-class support for handling semi-structured data like JSON, Avro, Parquet, etc.

Key capabilities:

· Load and store semi-structured data in their native formats

· Query semi-structured data directly without transformation

· Load semi-structured data into relational tables (flattening)

· Handle nesting and complex data structures

· Schema detection, manipulation, and evolution

· Convert between data formats (e.g. JSON to Avro)

1. What File Formats Does Snowflake Support for Loading and Unloading Data?

Snowflake supports loading and unloading data in multiple file formats:

· JSON, Avro

· Parquet

· ORC

· XML

· CSV files

· TSV files

It auto-detects schemas and data types of many file formats. Data can be loaded into Snowflake stages and then copied into tables from there.

1. What Snowflake Account Types Are Available?

Snowflake has 3 account types or editions:

· Standard Edition - For data warehousing and general workloads

· Business Critical - For critical workloads with the highest concurrency and availability

· Enterprise Edition - Has all the features. For complex workloads and large enterprises

All editions provide scalability, security, and high performance. Higher tiers have increased concurrency, availability, and advanced features.

1. Explain Snowflake Data Sharding.

Snowflake employs micro-partitioning and data sharding to scale performance and concurrency for large tables. Key aspects:

· Large tables split into small micro-partitions stored across multiple Snowflake compute nodes

· New partitions are added as data grows

· Partitions automatically pruned during query execution

· Enables scaling storage and computing separately

· Parallel queries can leverage partitioning

· Snowflake handles shard management automatically

1. What Is a Snowflake Pipeline?

In Snowflake, a pipeline refers to the architecture used for loading and transforming data. Key aspects:

· Handles moving data from sources into Snowflake

· Handles transformation, cleansing, and business logic

· Has stages for landing raw data

· Tables for storing transformed data

· Tasks, streams, and snow pipes form the pipeline

· ETL/ELT orchestration happens in pipelines before analytics

Pipelines enable large-scale cloud ETL.

1. How Can You Monitor and Optimize Snowflake's Performance?

Some ways to monitor and optimize performance are:

· Reviewing query history, and profiles to identify slow queries

· Checking warehouse utilization for optimal sizing

· Tuning queries to leverage clustering, partitioning

· Using appropriate caching for hot data

· Scaling up or down warehouses for concurrency

· Tracing long-running queries to identify bottlenecks

· Collecting statistics on tables for query optimization

· Checking usage patterns to optimize workloads

1. What Are Good Practices for Snowflake Security?

Some good practices include:

· Use role hierarchy for access control

· Grant least privileges needed using roles

· Enable MFA for administration

· Setup network policies for access control

· Encrypt sensitive data at rest and in transit

· Mask-sensitive data displayed

· Use client-side and server-side field-level encryption

· Configure auditing for access monitoring

· Regularly review user access and permissions

1. How Can You Achieve High Concurrency in Snowflake?

Some ways to achieve high concurrency are:

· Using multiple virtual warehouses to distribute load

· Leveraging micro-partitions and sharding architecture

· Cloning tables/databases to prevent conflicts

· Using resource monitors to monitor and optimize concurrency

· Scaling up/down warehouses automatically based on usage

· Caching hot data to reduce compute requirements

· Tuning queries to run efficiently in parallel

· Optimizing ETL/ELT pipelines for efficiency

1. What Are Snowflake Resource Monitors?

Resource monitors in Snowflake allow you to monitor and control the usage of resources like warehouses, data processed, credits used, etc. Key features:

· Set usage limits for resources

· Get notifications when limits are approached

· Configure auto-scaling actions like scaling warehouses

· Monitor parallel queries to optimize concurrency

· Historical monitoring for usage analysis

· Prevent overspending by setting credit limits

· Role-based access control for governance

1. Explain Clustering and Partitioning in Snowflake.

Clustering and partitioning in Snowflake help optimize large tables:

· Clustering co-locates related data for faster access

· Partitioning splits large tables across nodes

· Automatically managed during load and maintenance

· Queries prune unnecessary partitions/clusters

· Improves query performance by pruning scan data

· Scales storage and computing by splitting across nodes

· Parallel queries can leverage clustering and partitioning

1. How Can You Import and Export Data in Snowflake?

Snowflake provides several ingestion methods like loading from cloud storage and ETL/ELT pipelines. For export:

· CREATE FILE FORMAT to specify output format

· SELECT INTO command to unload data to the stage

· COPY INTO command to export query results

· Snowpipe continuously exports data from tables

· Query results can be downloaded directly

· Tables can be cloned to access identical data sets

1. What Are the Different Snowflake Account Types?

The 3 main account types offered by Snowflake are:

· Standard - For general workloads and smaller users

· Business Critical - For highly concurrent and business-critical workloads with higher availability

· Enterprise - Top tier with the highest concurrency, availability, and advanced features like data exchange, governed data sharing, etc.

All tiers provide scalability, security, and high performance. Higher tiers have increased capabilities and advanced features.

1. What Are the Different Authentication Methods Supported by Snowflake?

Snowflake supports several authentication methods:

· Username and password

· Okta

· SAML 2.0-based single sign-on (SSO)

· OAuth 2.0 authentication

· Multi-factor authentication (MFA)

· Key-pair authentication for programmatic access

· Federated authentication and external token-based authentication

1. How Can You Achieve High Availability in Snowflake?

Some ways to achieve high availability are:

· Use Business Critical or higher editions which have built-in HA

· Use multiple availability zones and regions for database failover

· Stream data to and from Snowflake for access from external sources

· Create failover databases for disaster recovery

· Use read replicas for read scalability and failover

· Implement client application retries and reconnections

· Monitor dashboards continuously for anomalies

1. What Are the Consideration When Selecting a Snowflake Region, Cloud Platform, etc.?

Some key considerations are:

· Data location and business presence for latency

· Cloud provider costs, discounts, and agreements

· Compliance and regulatory requirements

· High availability configurations and offerings

· Feature availability in regions like time travel, data sharing

· Network connectivity and bandwidth

· Disaster recovery and business continuity needs

Credit Analyzing

Wednesday, September 17, 2025

Capital Markets & Investment Management services and Trade Lifecycle and Investment Data Flow

1. Start with a high-level domain understanding

2. Connect business processes to data/technology

3. Show awareness of key challenges in the domain

4. Tie to your role (data engineering / analytics / ETL / reporting)

5. Give an example (STAR method)

1. Trade Lifecycle (End-to-End Flow)

2. Flow of Investment Data

3. How to Explain in Interview (Sample Answer)

📌 Capital Markets & Investment Management – Interview Cheat Sheet

1. Trade Lifecycle (Detailed)

2. Investment Data Flow (Detailed)

3. Combined 30-Second Pitch

Saturday, August 16, 2025

Python using AI

Python using AI - Prompts & Codes

Monday, June 23, 2025

Streaming data design

High-Level Architecture

Saturday, June 21, 2025

Snowflake QA

Capital Markets & Investment Management services and Trade Lifecycle and Investment Data Flow

Report Abuse

Wednesday, September 17, 2025

Capital Markets & Investment Management services and Trade Lifecycle and Investment Data Flow

1. Start with a high-level domain understanding

2. Connect business processes to data/technology

3. Show awareness of key challenges in the domain

4. Tie to your role (data engineering / analytics / ETL / reporting)

5. Give an example (STAR method)

You said:

ChatGPT said:

You said:

ChatGPT said:

You said:

ChatGPT said:

1. Trade Lifecycle (End-to-End Flow)

2. Flow of Investment Data

3. How to Explain in Interview (Sample Answer)

You said:

ChatGPT said:

You said:

ChatGPT said:

You said:

ChatGPT said:

You said:

ChatGPT said:

You said:

ChatGPT said:

📌 Capital Markets & Investment Management – Interview Cheat Sheet

1. Trade Lifecycle (Detailed)

2. Investment Data Flow (Detailed)

3. Combined 30-Second Pitch

Saturday, August 16, 2025

Python using AI

Python using AI - Prompts & Codes

Monday, June 23, 2025

Streaming data design

High-Level Architecture

Saturday, June 21, 2025

Snowflake QA

Capital Markets & Investment Management services and Trade Lifecycle and Investment Data Flow