An open table format is a set of rules for organising data files on cheap storage( like Amazon S3) so that they behave like proper database table. You get reliable updates, safe schema changes and “undo” features, while your data stays in open file formats that many tools can read. The best known open table formats are Apache Iceberg, Delta Lake and Apache Hudi.

Start with the problem

Modern data platforms often store data as files, usually in a columnar format called Parquet, sitting in cloud storage. This is cheap and flexible, and almost every analytics tool can read it. The approach is often called a data lake. But files in folder are not a database. Here is what goes wrong:

• Half-finished writes: If a job fails midway, readers may see some new files but not others, so the numbers are wrong.
• No safe Updates: Files can’t be edited in place. Changing or deleting one row means finding and rewriting the right files yourself.
• Two writers collide: If two jobs write at the same time, nothing stops them from overwriting each other.
• Slow Discovery: To answer “ which files belong to this table?”, a tool has to list every file in the folder, which gets slow with millions of files.
• Painful schema changes: Adding, renaming or removing a column can leave old and new files out of sync.

How an open table format works

Almost every open table format follows the same four ideas:

• Data fils stay as they are. Your rows are still stored in Parquet files. They never edited in place.
• Metadata Describes the table. Extra files record the table’s schema, how it is partitioned, which data files currently belong to it and statistics about each file.
• Each change creates a new version. Writing data produces new files and a new snapshot: a record of exactly which files make up the table at that moment. Old snapshots remain until you remove them.
• Commits are atomic. A job writes its new files first and only then makes them visible in a single all-or-nothing step. Readers see either the old version or the new one, never something in between. If two jobs try to commit at once, one notices the conflict and retries oor fails safely.

The benefits

• ACID transactions: Changes either fully happen or don’t happen at all, and concurrent users don’t see other’s unfinished work.
• Updates, deletes and merges: You can change individual rows with normal SQL.
• Schema Evolution: Add or Rename columns without rewriting everything.
• Time Travel: Query an earlier version of the table or rollback a mistake.
• Faster Queries: Engines use the metadata and file statistics to skip files that can’t contain the data they need, instead of scanning everything.

Do you need one?

An open table format is a good fit when:

• Your data lives in cloud object storage and you need to update or delete rows.
• Several jobs or people read and write the same tables.
• You want auditability, rollbacks or the ability to change schemas safely.
• You want several tools or engines to share the same data.

It may be overkill if you only append small static files that a single job reads, or if your data already lives happily in a traditional database or warehouse that meets your needs.

Open Table formats take the cheap, flexible world of data files and give it the reliability of a database: safe concurrent writes, row-level changes, schema evolution and time travel. They work by adding a layer of metadata that tracks exactly which files make up a table at each points in time and by committing changes atomically.

Open Table Formats for Data Engineering: A Beginner’s Guide was originally published in iTech Publication on Medium, where people are continuing the conversation by highlighting and responding to this story.

In this article, we’ll explore these NumPy techniques along with concepts like Advanced Broadcasting, Views vs Copies, and Stride tricks to help you write more efficient numerical code in Python.

Vectorization

This is the feature which answers why numpy is fast and also forms a builiding blocks for features like Broadcasting and Boolean Masking.

Vectorization means performing operations on entire arrays instead of loops.

// Traditional python approach

import numpy as np
arr=np.arange(10000)

result=[]
for x in arr:
      result.append(x*2)

//NumPy Vectorized Version 
result = arr * 2

//NumPy performs this in compiled C code, making it much faster.

Broadcasting

Broadcasting is a mechanism in Numpy that allows arrays of different shapes to perform arithmetic operations together without explicitly reshaping or copying data.
In simple terms, Numpy automatically expands the smaller array across the larger array so that element-wise operations like addition, subtraction or multiplication can be performed.
This eliminates the need of manual loops or repetitive data, making computations both faster and more memory-efficient.

//Broadcasting is even more powerful when working with 
//multi-dimensional arrays.
matrix = np.array([
    [1, 2, 3],
    [4, 5, 6]
])

vector = np.array([10, 20, 30])

result = matrix + vector
print(result)

//Output
[[11 22 33]
 [14 25 36]]

Boolean Masking

Numpy allows to select, filter or modify elements in an array based on a condition, this technique is Boolean Masking. Numpy allows you to generate a boolean array(True/ False) by applying a specific condition to entire array at once. This Boolean array acts as mask that determines which elements need to be selected.
Boolean masking supports creation of multi conditioned arrays. This can be practically used in Data Cleaning steps.

//Boolean masking is widely used in data preprocessing and cleaning.
import numpy as np
data = np.array([120, 150, -1, 200, -5, 180])

clean_data = data[(data>=0) & (data <200)]

print(clean_data)

//modifying using boolean masking 
import numpy as np
arr=np.array([10,30,48,-2,-5,1000])

arr[arr>15]=0

print(arr) // prints [10,0,0,-2,-5]

Fancy Indexing

Fancy Indexing allows you to select specific elements from an array using lists or arrays of indices. Unlike slicing the Fancy Indexing allows to select non- consecutive elements effortlessly.
It works efficiently with multi-dimensional array and also extracts the subsets quickly.

import numpy as np
arr=np.array([10,30,-34,4,67])

idx=[1,0,4]
result=arr[idx]
print(result) //[30,10,67]

// Fancy Indexing with Multi-dimensional arrays
matrix = np.array([
    [10, 20],
    [30, 40],
    [50, 60]
])

rows=[0,1,2]
cols[0,1,1]

result=matrix[rows,cols]
print(result) // [10,40,60]

Slicing vs Fancy Indexing
Fancy Indexing is usually used for selecting the non -contiguous elements from an array. Where as Slicing is used for selecting contiguous elements of an array.
Fancy Indexing returns the copy of the original array. On the other hand, Slicing returns the view of the original array meaning both the arrays share same memory.

Where Function

The np.where() function is used for conditional selection in NumPy arrays. It works like a vectorized if–else statement, allowing you to apply a condition to every element in an array and return values based on whether the condition is True or False.

//The basic syntax:
np.where(condition, value_if_true, value_if_false)

Instead of writing loops and conditional statements for each element, np.where() lets NumPy handle the operation efficiently across the entire array.

//It can be used for Data categorization
import numpy as np
scores=np.array([45, 72, 88, 33, 60])

result = np.where(scores >= 50, "Pass", "Fail")
print(result)

NumPy Views vs Copies

One important concept in NumPy that many beginners miss is the difference between views and copies of arrays. Understanding this helps you avoid unexpected bugs and unnecessary memory usage.

Copy: A copy creates a completely new array in memory. Changes to the copied array do not affect the original array.

import numpy as np

arr = np.array([1, 2, 3, 4])

copy_arr = arr.copy()

copy_arr[0] = 100

print("Original:", arr)
print("Copy:", copy_arr)

View: A view does not create new memory. Instead, it references the same underlying data.

arr = np.array([1, 2, 3, 4])

view_arr = arr.view()

view_arr[0] = 100

print("Original:", arr)
print("View:", view_arr)

Checking if an array is view

print(view_arr.base)
//If .base is not None, the array is a view of another array.

Conclusion

NumPy is much more than just arrays. Features like broadcasting, boolean masking, fancy indexing, and vectorization make it a powerful tool for numerical computing.

Mastering these techniques allows developers and data scientists to write high-performance code with minimal effort.

If you’re working with data analysis, machine learning, or scientific computing, these NumPy concepts are essential tools in your toolkit.

NumPy Beyond Basics: Broadcasting, Boolean Masking, Fancy Indexing and Powerful Array Techniques was originally published in iTech Publication on Medium, where people are continuing the conversation by highlighting and responding to this story.

What is Parquet, Really?

Apache Parquet is a columnar storage file format. That one word — columnar — explains almost everything about why it’s fast.
Traditional formats like CSV and JSON are row-oriented: they store data record by record, the way you’d type it into a spreadsheet.

//Traditional formats
id, name, age, city
1, Alice, 29, NYC
2, Bob, 34, LA
3, Carol, 41, SF

//Parquet flips this and stores data by column by column:
id: [1,2,3]
name: [Alice, Bob, Carol]
age: [29, 34, 41]
city:[NYC, LA, SF]

Why Columnar Storage Wins

1. You only read what you need. If your query is SELECT age FROM users, a row-based format still has to scan every row, pulling in id, name, and city you never asked for. Parquet reads only age column.
2. Compression gets dramatically better. A column of ages (29, 34, 41, 29, 34…) is far more repetitive and compressible than a mixed row of strings and numbers. Parquet applies type-specific encoding (dictionary encoding, run-length encoding, bit-packing) on top of compression codes like Snappy, Gzip or Zstd.
3. Predictive pushdown. Parquet stores min/ max statistics per column chunk, so query engines can skip entire blocks of data without reading them — e.g., “this row-group’s max age is 25, skip it if I am filtering for age > 30.”
4. Schema is embedded. Unlike CSV, a Parquet file knows its own column types. No more guessing whether “007” is a string or an integer.

Parquet vs CSV vs JSON vs Avro — The Real Comparison

Hands-On: Let’s Prove It With Numbers

Let’s stop talking and generate some real data. I’ll create a 1-million- row dataset and compare file size and query speed across formats.

Setup

import pandas as pd 
import numpy as np 
import time
import os 

# this will generate  1 million rows of real data 
np.random.seed(42)
n=1_000_000
df=pd.DataFrame({
    "user_id": np.arange(n),
    "age": np.random.randint(18,80,n),
    "country": np.random.choice(["US", "UK", "IN", "DE", "BR"],n),
    "signup_date": pd.date_range("2020-01-01",periods=n,freq="min"),
    "purchase_amount": np.round(np.random.uniform(5,500,n),2),
})
df.to_csv("data.csv",index=False)
df.to_json("data.json",orient="records", lines=True)
df.to_parquet("data.parquet",engine="pyarrow", compression="snappy")

for f in ["data.csv", "data.json", "data.parquet"]:
    size_mb=os.path.getsize(f)/(1024*1024)
    print(f"{f}: {size_mb:.2f} MB")

#Typical Output

data.csv: 52.10 MB
data.json: 98.40 MB
data.parquet: 11.30 MB 

# Numbers may change 

Query Speed: Reading a Single Column

CSV has to parse the entire file line by line even though we only wanted one column. Parquet jumps straight to th ecolumn’s byte range on disk. That’s roughly a 20x speedup — and the gap grows as the dataset grows.

start=time.time()
pd.read_csv("data.csv",usecols=["purchase_amount"])
print(f"CSV: {time.time()-start:.3f}s")

start=time.time()
pd.read_parquet("data.parquet",usecols=["purchase_amount"])
print(f"Parquet: {time.time()-start:.3f}s")

**********************
#Typical output
CSV: 0.480s
Parquet: 0.021s

Filtering With Predicate Pushdown

This is Parquet’s real superpower for big data. Using pyArrow, filters can be applied while reading, so unnecessary data is never ever loaded into memory.
There is no CSV equivalent to this. With CSV you must read everything into memory first, then filter with pandas. With Parquet, the filtering happens at the storage layer.

import pyarrow.parquet as pq
import pyarrow.dataset as ds

dataset=ds.dataset("data.parquet", format="parquet")

table =dataset.to_table(
  filter=(ds.field("country")=="IN") & (ds.field("age")>40),
  columns=["user_id", "age", "country"]
)

print(table.num_rows)

When Parquet Is Not the Right Choice

1. Frequent small writes/streaming inserts — Parquet files are meant to be written once and read many times. Constantly appending single rows is inefficient; use a row-based format (Avro) or a database instead, then batch-convert to Parquet periodically.
2. Human debugging — You can’t cat a parquet file and read it. You need a tool (parquet-tools, pandas, DuckDB) to inspect it.
3. Small datasets (a few KB-MB) — the overhead of Parquet’s metadata and schema isn’t worth it for a 200- row config file. CSV or JSON is simpler.
4. Nested/ Rapidly changing schemas from external APIs — JSON is often more convinient during ingestion, then converted to Parquet afterward.

Final Thoughts

Parquet isn’t just a “a faster CSV.” It’s a fundamentally different way of thinking about how data sits on disk — optimized for the reality that most analytical queries touch a handful of columns and millions of rows, not the other way around.
If you are still defaulting to CSV for anything beyond a quick export, the numbers above make a pretty strong case for switching. Try it on your own dataset — the size and speed difference is usually immediate and obvious.

Parquet Files Explained: Why Every Data Engineer should Use was originally published in iTech Publication on Medium, where people are continuing the conversation by highlighting and responding to this story.

If you have ever tried to navigate the Meta Developer Ecosystem, you know it can feel like a maze. Between Business Portfolios, App Scopes, and Webhook handshakes, things can get confusing quickly.

Recently, I took on the challenge of integrating Meta’s WhatsApp Cloud API with an Odoo ERP backend for an enterprise use case (Ipawa Tech Solutions). The goal was to build a seamless communication pipeline where client WhatsApp messages route directly into Odoo’s CRM and project chatter, and automated service updates route back to the client.

It was a journey filled with classic Meta redirects, hidden settings If you are trying to wire up this integration yourself, here is the exact, error-free roadmap of how I did it so you can get it right on the first try.

The Big Picture Workflow

Before diving into the code and configurations, it helps to understand how these tools talk to each other:

+-----------------------+                    +-------------------------+
  |  Meta Developer App   | <--- Webhook ----> |    Odoo ERP Backend     |
  | (WhatsApp Cloud API)  |                    | (WhatsApp Module/Apps)  |
  +-----------------------+                    +-------------------------+
              ^                                             ^
              | (Sandbox Mode)                              |
              v                                             v
     [Verified Test Phone]                        [Contacts & Discuss App]

Phase 1: Navigating the Meta Developer Maze

The journey started on the Meta for Developers platform.

1. Creating the App Blueprint

First, I created a new application. Meta forces you to choose a use case framework right out of the gate. For this integration, select Connect with customers through WhatsApp.

2. The Business Portfolio Link

During creation, you have to link the app to a Meta Business Portfolio. If your UI boots you out to your personal Facebook feed after clicking create (a classic Meta glitch!), don’t panic. Simply head back to developers.facebook.com, click My Apps in the top right, and your newly created app workspace will be waiting for you.

3. Activating the WhatsApp Product

Inside the app dashboard:

• Navigate to Use cases in the left sidebar.
• Find the WhatsApp card and click Set Up.
• Pro Tip: If the dashboard screen looks completely blank after setting it up, click Quickstart right above API Setup to run Meta’s 1-minute initialization wizard. This forces the cloud environment to spin up.

Once initialized, navigate to WhatsApp > API Setup. Keep this tab open — this is your goldmine for credentials.

Phase 2: Preparing the Odoo Engine

With the Meta keys ready, it was time to jump into the Odoo backend.

1. Installing the Dependencies

By default, Odoo’s settings won’t show WhatsApp configurations until the underlying application module is active.

• Head to the Odoo Apps dashboard.
• Clear the default “Apps” search filter, type WhatsApp, and hit the purple Activate/Install button.

2. Mapping the API Credentials

Once the module finishes installing, go to Settings, search for WhatsApp in the top utility bar, and click → Configure WhatsApp Business Accounts. Click New to open a clean provider profile.

This is where the magic happens. I mapped the credentials from the Meta Developer tab into Odoo using this exact matrix:

Odoo Configuration FieldMeta Developer Workspace Source LocationAccount NameIpawa Tech Solutions (or your custom business name)App IDProminently displayed at the very top header bar of your Meta App.App SecretGo to App settings > Basic on Meta. Click Show (requires your FB password).TokenThe Temporary Access Token sitting at the top of the API Setup panel.Phone Number IDFound under Step 1 of the Meta API Setup page.WhatsApp Business Account IDFound directly below the Phone Number ID in Step 1.

With everything filled out, I hit Test Credentials in the bottom left. When that connection status icon flashes green, it’s an incredibly satisfying feeling!

Phase 3: The Webhook Handshake (Two-Way Sync)

Getting Odoo to send messages is only half the battle. We also need Odoo to receive messages when a customer replies. This requires a secure Webhook handshake.

Once Odoo successfully tests the credentials, it automatically generates two unique values at the bottom of the form: a Callback URL and a Verification Token.

1. Copy those values and switch back to the Meta Developer tab.
2. On the left sidebar under WhatsApp, click Configuration.
3. In the Webhooks section, click Edit, paste the URL and token from Odoo, and click Verify and Save.

⚠️ The Crucial Step Most People Miss: After saving the webhook, a list of Webhook fields will appear. Look for the field named messages and click Subscribe. If you don’t turn this on, Meta will never forward incoming client messages back to your Odoo database!

Phase 4: Overcoming Sandbox Restrictions & Live Testing

Because my Meta application is still in Development Mode, Meta locks down the environment to prevent accidental spam. Testing required navigating two explicit safety rules:

1. Whitelisting the Test Number

The sandbox will only talk to phone numbers you explicitly authorize. On the Meta API Setup page, find the To field dropdown under Step 1, click Manage phone number list, add your personal phone number, and type in the verification code Meta sends you via WhatsApp.

2. Synchronizing Business Templates

Meta prohibits businesses from sending arbitrary, plain-text messages to open a new conversation thread. You must use a pre-approved template.

• In Odoo, inside your WhatsApp Provider profile, click the 🔄 Synchronize Templates button at the top left.
• Odoo instantly fetches Meta’s default sandbox templates (like the standard welcome greeting) and updates their status to Approved.

3. Firing the Live Test!

With everything wired:

1. I opened the Odoo Contacts app and created a test contact using my verified personal number (formatted with the country code, e.g., +254...).
2. Clicked the inline WhatsApp action icon on the contact file.
3. Selected the newly synchronized welcome template and hit Send.

Within seconds, my phone buzzed with the template message!

To test the inbound loop, I texted back: “Systems are live!”. I went over to the Odoo Discuss app, and there it was — a brand new live chat thread containing my text response, completely integrated into the ERP ecosystem.

Key Takeaways From This Build

1. Mind the Token Expiration: Temporary developer tokens provided by Meta expire every 24 hours. For a production environment, you’ll need to upgrade to a Permanent Access Token via a Meta System User profile.
2. Subscribing is Key: A green webhook test configuration doesn’t mean data is flowing. Never forget to manually subscribe to the messages event webhook field.
3. Architecture Matters: Keeping business logic inside Odoo while offloading the communication transport layer to Meta keeps the architecture incredibly clean and scalable.

Building this integration taught me exactly how rigid, yet powerful, enterprise API frameworks can be. Next up on my radar: configuring Odoo’s automated sales workflows so that when a new customer completes an order, it auto-generates developer tasks and drops an update directly to their WhatsApp.

Integrating Meta’s WhatsApp Cloud API was originally published in iTech Publication on Medium, where people are continuing the conversation by highlighting and responding to this story.