Datum API

18/05/2024

🔹 Medallion Architecture in Data Lakehouse 🔹

Transform your data journey from raw to refined with the Medallion Architecture! 🌟

✨ Bronze Layer: Raw data from external sources, capturing every detail. This layer ensures quick Change Data Capture, providing a historical archive of source data with complete lineage and auditability. 📂

✨ Silver Layer: Cleansed and conformed data, perfect for self-service analytics. This layer merges and cleans data just enough to provide an Enterprise view, enabling advanced analytics and machine learning for various business entities and transactions. 🧹📊

✨ Gold Layer: Curated, ready-to-use data for in-depth analysis and reporting. The final transformations and quality rules are applied here, creating project-specific, read-optimized databases for reporting and analytics. 🏆🔍

With tools like Databricks' Delta Live Tables, building these pipelines is a breeze! Create streaming, incremental updates for real-time insights with the power of Apache Spark™️ Structured Streaming. 📊🚀

Why it’s awesome:

Simple and scalable model: Easy to understand and implement. 🛠️
Incremental ETL for agility: Streamlined data processing with minimal transformations. ⚡
ACID transactions & time travel for reliability: Ensures data integrity and allows you to recreate tables from raw data anytime. 🔄
Unlock the full potential of your data with this structured approach! 🌐💡

10/03/2024

Working Directory:
Your project begins here. The working directory is where you actively make changes to your files.

Staging Area (Index):
After modifying files, use git add to stage changes. This prepares them for the next commit, acting as a checkpoint.

Local Repository:
Upon staging, execute git commit to record changes in the local repository. Commits create snapshots of your project at specific points in time.

Stash (Optional):
If needed, use git stash to temporarily save changes without committing. This is particularly useful when switching branches or performing other tasks.

Remote Repository:
The remote repository, typically hosted on platforms like GitHub, serves as a version of your project accessible to others. Use git push to send local commits to the remote repository, and git pull to fetch changes from the remote.

Remote Branch Tracking:
Local branches can be set to track corresponding branches on the remote repository. This facilitates synchronization with git pull to fetch remote changes or git push to send local changes to the remote repository.

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ETL, Data Pipeline, Data Warehousing, Big Data, Data Integration, Data Modeling, Streaming Data, Data Cleansing, Batch Processing, Data Governance, Data Lake, Data Architecture, Data Transformation, Data Quality, Data Warehouse Design, Real-time Data Processing, Data Migration, Data Analysis, Data Engineering Tools, Data Security
Pipeline Warehousing Data Integration Modeling Data Cleansing Processing Governance Lake Architecture Transformation Quality Warehouse Design -time Data Processing Migration Analysis Engineering Tools Security

28/02/2024

Database Big Data Programming Cloud Data Processing & Analysis Data Visualization Tools Machine Learning Tools Data Analytics Tools Data Modeling Data Engineering Tools Data Warehousing

19/02/2024

17/02/2024

𝗨𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱𝗶𝗻𝗴 𝗲𝘅𝗲𝗰𝘂𝘁𝗶𝗼𝗻 𝗼𝗿𝗱𝗲𝗿 𝗼𝗳 𝗦𝗤𝗟 𝗾𝘂𝗲𝗿𝗶𝗲𝘀!

Before you can optimize SQL queries, you must understand their order of ex*****on!

The order of ex*****on is different from how you write it, here's the actual order:

1️⃣ 𝗙𝗥𝗢𝗠: Determines the tables of interest

2️⃣ 𝗝𝗢𝗜𝗡: Joins the tables of interest as per specification and sets up the base data.

2️⃣ 𝗪𝗛𝗘𝗥𝗘: Applies a filter to the rows returned from the FROM clause. It restricts the result set to only those rows that meet a specified condition

3️⃣ 𝗚𝗥𝗢𝗨𝗣 𝗕𝗬: Groups rows that have the same values in specified columns. It is often used with aggregate functions ( eg. COUNT, MAX, MIN, SUM, AVG) to perform calculations on each group.

4️⃣ 𝗛𝗔𝗩𝗜𝗡𝗚: Similar to the WHERE clause, but it is used to filter groups based on aggregate functions. It is applied after the GROUP BY clause. For instance, "HAVING COUNT(*) > 10" would only include groups having more than 10 rows.

5️⃣ 𝗦𝗘𝗟𝗘𝗖𝗧: Used to specify the columns from the filtered results to display in the query's result set. It can include column names, aggregate functions, and expressions.

6️⃣ 𝗢𝗥𝗗𝗘𝗥 𝗕𝗬: Sorts the result set returned by the query in ascending (ASC) or descending (DESC) order based on one or more columns.

7️⃣ 𝗟𝗜𝗠𝗜𝗧: Restricts the number of rows returned by the query.
_________
That's a wrap!

13/02/2024

Embark on a mesmerizing journey through the Time Odyssey, where the peaks and valleys of economic flux unveil the heartbeat of our financial landscape. 📈💫 From the dizzying heights of prosperity to the depths of recession, every fluctuation narrates a tale of resilience, adaptation, and growth. 🌊⏳ Through this captivating time series, witness the ebb and flow of market forces, reflecting the intricate dance between supply and demand, innovation and regulation. Explore the patterns that shape our economic destiny, as history repeats itself in a cycle of boom and bust, expansion and contraction. 🔄💼 Let this chronicle of time serve as a guide, illuminating the past, present, and future of global finance. 🌐🔍 Discover the secrets hidden within the data, as trends emerge and fade, leaving their mark on the canvas of prosperity. 💡📊 Join us on this odyssey of discovery, where each data point is a beacon of insight, guiding us through the uncertainty of tomorrow with the wisdom of yesterday. 🚀🔮 "

07/02/2024

Relational Databases:
Relational databases are the traditional workhorses of the data world. Structured around tables with rows and columns, they enforce a predefined schema. Examples include MySQL, PostgreSQL, and Oracle Database. Relational databases are ideal for scenarios where data has well-defined relationships and consistency is key, such as managing financial records, customer information, and inventory systems.

NoSQL Databases:
NoSQL databases offer a departure from the rigid structure of relational databases. They are designed to handle unstructured, semi-structured, and polymorphic data with ease. NoSQL databases come in various flavors, including document-oriented (e.g., MongoDB), key-value stores (e.g., Redis), wide-column stores (e.g., Cassandra), and graph databases (e.g., Neo4j). They shine in scenarios requiring scalability, real-time analytics, and rapid iteration, making them popular choices for social networks, IoT applications, and content management systems.

Graph Databases:
Graph databases are specialized in managing relationships between data entities. They represent data as nodes, edges, and properties, making them ideal for scenarios involving complex networks and highly interconnected data. Examples include Neo4j, Amazon Neptune, and TigerGraph. Graph databases are particularly useful for applications like social network analysis, fraud detection, and knowledge graphs.

04/02/2024

Cloud Demystified: AWS vs Azure vs Google ☁️

Introduction

Lost in the cloud maze of AWS, Azure, and Google Cloud? Fear not! Let's dive into the core compute products and navigate the purchasing options. Ready for a cloud adventure? 🚀

Understanding Compute

Compute products form the backbone of cloud bills. Let's explore a quick comparison across the big three.

Purchasing Options

Reservations

AWS Reserved Instances, Azure VM Reservations, and Google Committed Use discounts offer discounts for advanced capacity purchase.
AWS has Convertible Reservations, Azure offers flexibility, and Google is more chill but no turning back.
Spot & Preemptible Instances

AWS spots, Azure's low-priority VMs, and Google's preemptible VMs offer discounts for unused capacity.
The catch? Instances can be evicted for higher-paying users. Ideal for short-lived processes.
Sustained Use Discounts

Google Cloud introduces Sustained Use Discounts, automatically giving larger percentage-offs.
Watch out for GCP prices; they're already discounted assuming full-month usage.
Market Share & Perception

AWS dominates at 47%, Azure at 22%, and Google at 7%. Numbers aside, perception matters. Azure may seem stodgy, Google Cloud slick but less performant, and AWS a pioneer. Familiarity plays a significant role.

Do the Differences Matter?

While variations exist, providers and offerings are often equivalent. Specific business needs may be deciding factors. Prepare for a multi-cloud reality, ensuring application portability and avoiding vendor lock-in.

Conclusion

In the vast cloud expanse, choices abound. Embrace the diversity, tailor your selection, and ride the wave of technological possibilities. The cloud, like the sky, has room for everyone. 🌥️✨

23/01/2024

Digging into the quirks of Insertion Sort like a code archaeologist! 🤓🔄 Insertion Sort is the underdog of sorting algorithms, quietly doing its sorting dance. Let's unmask this sorting superhero:

Pros:
1️⃣ Simple and Intuitive: Insertion Sort's logic is so simple even your grandma could understand it, making it the Dumbledore of sorting algorithms.
2️⃣ Efficient for Small Lists: It's the ninja of small datasets, silently rearranging elements like a cat burglar in the night.
3️⃣ In-Place Sorting: Minimalism at its finest! Insertion Sort is the Marie Kondo of algorithms – keeping things tidy without adding extra baggage.

Cons:
1️⃣ Inefficiency with Large Lists: When faced with a massive to-do list, Insertion Sort might procrastinate a bit with its time complexity of O(n^2).
2️⃣ Not Ideal for Random Lists: Like finding matching socks in a drawer of chaos, Insertion Sort struggles with truly random lists.
3️⃣ Lesser Known Optimization Techniques: It's the unsung hero, maybe not flaunting the latest fashion, but hey, it gets the job done.

Best Practices:
1️⃣ Use for Small Lists: Think of Insertion Sort as your sorting buddy for short shopping lists – quick, simple, and gets the job done before you finish your coffee.
2️⃣ Combine with Other Algorithms: Mix and match like a fashionista! Pair Insertion Sort with other algorithms for a custom-tailored sorting experience.
3️⃣ Educational Purposes: Teaching sorting algorithms? Throw in a bit of humor, maybe a joke or two, and Insertion Sort becomes the class clown of algorithms.

Embrace the chaos-sorting ninja within Insertion Sort when life hands you small, nearly-sorted lists! 🧙‍♂️💻🚀

18/01/2024

10/01/2024

🔍 Explore the API Testing Landscape: Unveiling Key Approaches

Discover the diverse world of API testing, where different methods serve unique purposes in ensuring robust functionality. Here's a concise breakdown of four common approaches, allowing teams to tailor their API testing strategy:

Contract Testing:

Purpose: Validate adherence to API contracts.
Method: Checks content and format of requests/responses.
Benefits: Safeguard against contract violations in new releases.
Unit Testing:

Purpose: Confirm correctness of individual endpoints.
Method: Validates responses to specific requests.
Benefits: Ensures proper handling of parameters and error messages.
End-to-End Testing:

Purpose: Validate user journeys involving multiple endpoints.
Method: Chains requests to confirm seamless workflows.
Benefits: Identifies issues in complex scenarios before reaching users.
Load Testing:

Purpose: Confirm API reliability under peak traffic.
Method: Simulates large request volumes for response analysis.
Benefits: Proactively assess performance before critical events.
While these are core categories, the beauty of API testing lies in customization, allowing teams to craft a tailored strategy to meet their specific needs. 🌐✨