The Young DBA

 The role of a Data Engineer is shifting from writing boilerplate code to architecting intelligent systems. For the readers of youngdba.com, here is how Generative AI is fundamentally changing our landscape: 1. Beyond Coding: The Productivity Leap GenAI isn't just about finishing your Python scripts. It’s about Legacy Code Conversion (e.g., migrating old stored procedures to Spark) and Automated Documentation. What used to take hours of manual mapping can now be scaffolded in seconds, allowing us to focus on data quality and system design. 2. The Rise of Vector ETL As Architects, we are no longer just moving rows and columns. We are now managing Unstructured Data (PDFs, logs, images) and transforming them into Vector Embeddings. Integrating Vector Databases into our ETL pipelines is becoming a core competency for modern data platforms. 3. Data Quality & Synthetic Data One of the biggest hurdles in Data Engineering is testing with realistic data without compromising privacy. Gen...

Congratulations ! You’ve made the leap from star individual contributor to technical manager. You are no longer just responsible for your code; you are responsible for a team, their focus, their output, and their sanity. Among the many new skills you need to master—delegation, conflict resolution, and strategic thinking—there is one that often feels the most uncomfortable yet is the most critical to your success: handling the word "No." For many technical professionals, our instinct is to be problem-solvers. We like to say "yes." "Yes" means we can build it. "Yes" means we can fix it. "Yes" means we are helpful. But as a manager, always saying "yes" is a trap. It leads to scope creep, burnt-out teams, missed deadlines, and a dilution of your strategic goals. Mastering the "No" —both saying it and receiving it—is not about being stubborn or difficult. It is about protecting your team’s focus and ensuring you are deliv...

Think of the QUALIFY clause as the secret shortcut of the SQL world. While most of us grew up nesting subqueries just to filter the results of a window function, QUALIFY lets you do it in a single step. It acts on window functions exactly how HAVING acts on GROUP BY aggregations. Why It’s a Game Changer Cleaner Code : It eliminates the "Subquery Pyramid of Doom." No more wrapping a SELECT inside a SELECT just to get the ROW_NUMBER() = 1. Logical Flow: It filters results after window functions are calculated but before the final ORDER BY and LIMIT. Performance : Engines like Snowflake, BigQuery, and Teradata can optimize the execution plan better when the filter is explicit. The Syntax in Action Instead of writing 15 lines of CTEs to find the most recent login for every user, you can simply write: SELECT user_id, login_time, device_type FROM user_logs QUALIFY ROW_NUMBER() OVER (PARTITION BY user_id ORDER BY login_time DESC) = 1;

  The  India AI Impact Summit 2026 , held from February 16–20 at Bharat Mandapam in New Delhi, has emerged as a landmark event for the Global South. As of today, February 19, 2026, the summit is nearing its conclusion with several historic outcomes focused on democratizing technology and sovereign AI development.   The summit, themed " People, Planet, and Progress ," has moved beyond theoretical debate into concrete financial and structural commitments. 1. Major Financial & Infrastructure Commitments The most tangible outcome is the massive scale of investment and infrastructure pledged to make India a global AI powerhouse:   **₹20,000 Crore Investment: Global and domestic investors finalized commitments exceeding ₹20,000 crore to be deployed over the next two years into India's AI ecosystem.   GPU Democratization : Under the IndiaAI Mission, the government announced the onboarding of over 38,000 GPUs. Crucially, these are being made available...

We can do backup and restore on instance and DB level both. on-demand backup and automatic backup are possible at instance level only which we can configure Google Cloud Console. Database level there is no options and features available for full and differential backup like SQL server in cloud SQL database. We can do this task with Export feature this is similar to full backup and this we can do from console or portal. If we want to do automated DB export (full backup) then we should design ourselves with the help of services . Google provide some services like Google Function and Scheduler which we can use for this purpose as look like following architecture. Here is following steps which we can use to setup Create a bucket in Google Cloud Storage. Create Cloud Function to export a Cloud SQL database Grant Permission for the Cloud Function to access Cloud SQL export Test out the Cloud Function Create Cloud Scheduler Job to trigger the Cloud Function once a week  For detail or step...

The storage engine is the core component of  any database which is responsible for handling  from disk to memory. Mongodb Support multiple storage engine, Choosing the appropriate storage engine for your use case can significantly impact the performance of your applications. WiredTiger Storage Engine In-Memory Storage Engine MMAPv1 Storage Engine MongoDB removes their initial storage engine MMAPv1 and now WiredTiger Storage Engine is default storage engine. So we should understand what the reason is behind these changes. MMApV1 storage engine : It allows Concurrency control level on collection, so write performance is just good. This engine does not allow Compression support. WiredTiger Storage Engine :  Concurrency control in wiredtiger is maintaining on document level. It uses WiredTiger uses MultiVersion Concurrency Control (MVCC) like other RDBMS, So write performance is excellent.  Yes this engine allows compression. With WiredT...

Either Datawarehouse or BigData, ETL or ELT is the major part, or you can say it plays a vital role in Data Transformation Life cycle. ELT-ETL used in any kind of data related operations like, Data migration, Data transformation , Business intelligence, Datawarehouse, Big data and Analytics. ETL is Extract transform and Load whereas ELT is extract load then transform. Both have their own pros and cons but now a days ELT is more popular than ETL. This is why because size of data is growing & growing for transformation. ELT also supporting Data virtualization concept where actual original data will reside on system without any modification and transformation will less. SQL server has reach sets of tools for ETL & ELT .  Here I am sharing some important Differences between ETL and ELT which we should know before jumping on features. SrlNo Dimension ETL ELT 1 Technology Adoption ETL is a well-developed process used for over 20 years ELT is a new technology ...

Now we can create static and dynamic both kind of schema relational table so a new kind of heterogeneous data structure can be created in Relational tables.  Relational database that is much more concerned with standards compliance and extensible than with giving you freedom over how you store data. It uses both dynamic and static schema and allows you to use it for relational data and normalized form storage. Static Relation data structure in Postgres CREATE TABLE Project (  id serial PRIMARY KEY,  name varchar,  mgr integer,  is_active boolean ); CREATE TABLE Task(  id serial PRIMARY KEY, name varchar, status boolean, project_id integer, CONSTRAINT idx_project_id FOREIGN KEY (project_id) REFERENCES project (id) ); Dynamic Relation data structure in Postgres CREATE TABLE IF NOT EXISTS project ( id serial PRIMARY KEY,  name varchar,  mgr integer,  tasks jsonb , is_active boolean ); Keeping JSON Document inside Postgres Table ...

Hi friend, I am continuing my post over my series article "Is it time to revisit PostgreSQL and MySQL with JSON support"  . Today I am sharing my 4th post which is related to " Changes in RDBMS after JSON " JSON bring changes on various vendors and technologies so new kind of challenges came to introducing this feature in their technology. To cover-up these challenge RDBMS started inclusion of JSON data type in their engine. Sooner or later all major RDBMS vendor (eg PostgreSQL, mySQL and SQL Server) added JSON as a data type to keeping document in relational table.  These are the database version when JASON introduced :- . Technology Postgres mySQL SQL Server Version PostgreSQL 9.3 MySQL 5.7.8 SQL 2016 Now a new heterogeneous database kind of structure started creating with relational engine. Static and dynamic data structure in RDBMS started introducing. Mean Relational Table =Normalization + denormalized (JSON/Array).  JSON data Passing done via stan...

It’s 3rd day of Lock down in India . Today I started morning with some tips that will help to working from home during lockdown. Here I am going to share top 10 Tips while working from home due to corona virus pendamic outbreak . Avoid working while lying in a bed or relaxing in a couch/sofa/reclining chair. These comfortable setups will make it difficult to focus on work. Ideally a study table and chair would be conducive to staying focused and maintaining productivity. Maintain a routine, get up at a reasonable time, shower and have breakfast/lunch at the usual times. Also ensure you log off at reasonable times and get sufficient sleep at night. Try and emphasize to your family that work from home times are not holidays and request them to keep interruptions to a minimum. Take a 2-3-minute break from work every 30-45 minutes to just stretch, walk around or do some simple exercises. Setup and maintain regular meetings with your managers and teams. Keep people updated abou...

This is the 3rd post of my series article " Is it time to revisit PostgreSQL and MySQL with JSON support"   There are following impacts json bring in the technologies. NoSQL Document database became popular, mongodb was among one of lucky database vendor.  Technology found JSON as an alternate to xml for data interchange on platform independent. Technology found a supporting for RDBMS to keeping variety of data specially non-structured data in one environment and module. Data explosion and big data came into the platform. The cost on data keeping has rapidly decreased over the last several years, which has resulted in the rapid expansion of genomic data acquisition.  While new database technologies have become common to manage big data. Schema less architecture given support in fast and rapid application development. Polyglot persistence increases the technology surface area enough that it can become quite difficult to monitor, manage, develop, and operate...

After 2005 applications & user requirements started growing rapidly, Hardware and software developed, the advent of Single Page Applications and modern mobile/web apps that we know today needed some kind of data interchange to function seamlessly. To fulfill user requirement technology started shifting into  new language-independent data interchange format that time JSON came into the Picture. JSON gained rapid popularity because it makes transferring data very easy. It’s also lightweight and easy to read and understand. There are few other reasons that JSON make buzzword after 2005. Flexibility for rapid application development requirement APIs (application programming interfaces) Modern Web & Mobile Applications. Need of faster data travelling on various kind of devices. Big Data and variety in data.

Hi Friend, This is the time is your most important time in some productivity. Where the world is suffering from Pandemic of Corona disease (Covid-19). Everyone is under restrained in their home like jail. Govt of India told to stay in home for 21 days. So I decided to share my long pending post in a series. One of my favourite articile I wrote one year back, I got chance to share. I will share this as a series. Here are some topics related to my current Article  " Is it time to revisit PostgreSQL and MySQL with JSON support " . JSON stands for JavaScript Object Notation, and was first formalized by Douglas Crockford. JSON is a data format interchange - method of storing and transferring data. Mostly its uses such as data conversion (JSON to SQL) and exporting data from proprietary web apps or mobile apps. XML was a big buzzword in the early 2000’s, JSON become the buzzword in later few years.  Why JSON came into picture. What are the impacts JSON bring in database t...

When storing data in main memory, gives performance benefits but it is an expensive method of data storage. An approach to realizing the benefits of in-memory storage while limiting its costs is to store the most frequently accessed data in-memory and the rest on disk. This approach is called hybrid architecture of in-memory with Disk. There are some benefits of using hybrid architecture listed below: Performance (which is enhanced by sorting, storing and retrieving specified data entirely in memory, rather than going to disk). Lower TCO from reduction in hardware investment Cost, because a less costly hard disk can be substituted for more memory. Reduced point of failure using Persistence - All data is stored and managed exclusively in RAM is at risk of being lost upon a process or server failure. So every In-memory database  provide solution as a persistence to disk. This is usually act like hardening transaction log from memory to disk. Different vendor have different mec...

Now-a-days almost every relational or non-relational database gives in-memory capability. Traditional database vendor gives this capability using add-on in-memory engine and some are also specialized vendors. MySQL have in-memory table engine,  SQL server have in-Memory OLTP engine,  SAP has HANA,  Oracle have TimesTen,   Mongodb have separate in-memory services.  These all are version and edition dependent. To use this we need extra licensing cost. Apart from that there are some popular specialized in-memory database vendor like  Redis,  Aerospike,  VoltDB and  Arangodb.  Thanks for reading Plz dont forget to like Facebook Page.. https://www.facebook.com/pages/Sql-DBAcoin/523110684456757

An in-memory database (IMDB) is a database whose data is stored in main memory to get faster response times. Currently there are 3 most popular storage systems for data/database, RAM, SSD and HDD. RAM performs random reads/writes at 100 MB per second. Sequential reads/writes are even faster 1 GB per second and higher. An SSD is slower than RAM. Random access time is a fraction of a millisecond (1e-5 seconds): we can randomly read around 10,000 blocks per second. Random access time is 10 milliseconds (1e-3 seconds), which is 100 times slower than an SSD. If we talk about price on average, each storage type is ten times more expensive than the previous one. An HDD is the cheapest storage type, an SSD is ten times more expensive than an HDD, and RAM is ten times more expensive than an SSD. So If we need access time of 1 ms or less, choice is RAM. Different database vendor have different size limits. There some popular use cases where In-memory database is being use. 1. Sessi...

We can create database server architecture similar to Oracle/SQL as well as mysql and also feature of mongodb. Postgres Supports all sorts of performance optimization that we used in Oracle or SQL Server. I think in that's case where MySQL is lacking. Supports materialized views and temporary tables. Supports temporary tables but does not support materialized views. It implements the SQL standard very well. It includes support for "advanced" SQL stuff like window functions or common table expressions (now supported in MySQL 8.0) . Postgres is very innovative in the matter of how plpgsql interacts with SQL. It supports lots of advanced data types, such as (multi-dimensional) arrays, user-defined types, etc. MySQL is partially compliant on some of the versions (e.g does not support CHECK constraints). PostgreSQL is widely used in large systems where read and write speeds are crucial and data needs to validated. In addition, it supports a variety of performance op...

MongoDB Compass allows users to easily analyze and understand the contents of their data collections within MongoDB and perform queries, without requiring knowledge of MongoDB query syntax. MongoDB Compass provides users with a graphical view of their MongoDB schema by randomly sampling a subset of documents from the collection. Sampling documents minimizes performance impact on the database and can produce results quickly. Features of Compass 1. Real Time Server Stats. 2. View, add, and delete databases and collections. 3. View and interact with documents with full CRUD functionality. 4. Build and run ad hoc queries 5. View and optimize query performance with visual explain plans 6. Manage indexes: view stats, create, and delete. 7. Create and execute aggregation pipelines. Thanks for reading Plz dont forget to like Facebook Page.. https://www.facebook.com/pages/Sql-DBAcoin/523110684456757

It's my 2nd post on PowerBI and Data Virtualization. PowerBI is one of the most popular software as service for Data Virtualization. Today I am sharing how to embed the powerbi report with other external application like html page . To embed start with downloading powerbi. 1.) Download- Install PowerBi Desktop and start flying with Data. ·          Install from Microsoft portal. ·          Connect to heterogeneous data-sources data sources (structure, un-structure, data-warehouse, hadoop, Data Lake, aws s3, azure blob). ·          Design and run a report (static and interactive quick and fast ). 2.) Enable Subscription on PowerBI cloud Service ·          Signup of Powerbi Service app with pro subscription (60 Days trail) 3.) Backup to powerbi desktop and sign-in powerbi service account. ·    ...