#dbms search results

𝗙𝗿𝗼𝗺 𝗘𝗥𝗗 𝘁𝗼 𝗔𝗣𝗜 🏏 Designed a complete IPL management system from scratch today Started with ER diagram → entities, relations, junction tables Converted it into models and structured database properly Then moved to routes and actual backend flow #DBMS #ChaiCode

devwithjay's tweet image. 𝗙𝗿𝗼𝗺 𝗘𝗥𝗗 𝘁𝗼 𝗔𝗣𝗜 🏏

Designed a complete IPL management system from scratch today

Started with ER diagram → entities, relations, junction tables

Converted it into models and structured database properly

Then moved to routes and actual backend flow

#DBMS #ChaiCode
devwithjay's tweet image. 𝗙𝗿𝗼𝗺 𝗘𝗥𝗗 𝘁𝗼 𝗔𝗣𝗜 🏏

Designed a complete IPL management system from scratch today

Started with ER diagram → entities, relations, junction tables

Converted it into models and structured database properly

Then moved to routes and actual backend flow

#DBMS #ChaiCode
devwithjay's tweet image. 𝗙𝗿𝗼𝗺 𝗘𝗥𝗗 𝘁𝗼 𝗔𝗣𝗜 🏏

Designed a complete IPL management system from scratch today

Started with ER diagram → entities, relations, junction tables

Converted it into models and structured database properly

Then moved to routes and actual backend flow

#DBMS #ChaiCode

🚀 Database Management System (DBMS) is the backbone of every software system. I’ve made handwritten DBMS notes covering all key concepts. #SQL #Database #DBMS #HandwrittenNotes Sharing a few free pages here 🧵👇 1/n

BharukaShraddha's tweet image. 🚀 Database Management System (DBMS) is the backbone of every software system.

I’ve made handwritten DBMS notes covering all key concepts.

#SQL #Database #DBMS #HandwrittenNotes

Sharing a few free pages here 🧵👇
1/n

𝗗𝗮𝘁𝗮 𝘀𝗶𝗱𝗲 𝗯𝗲𝗴𝗶𝗻𝘀 📊 • Creating databases and tables • Inserting and selecting data • Filtering records • UPDATE table • DELETE with WHERE • ALTER TABLE operations • DISTINCT, sorting, LIMIT, OFFSET • Aggregations • GROUP BY #DBMS #SQL #Backend #ChaiCode

devwithjay's tweet image. 𝗗𝗮𝘁𝗮 𝘀𝗶𝗱𝗲 𝗯𝗲𝗴𝗶𝗻𝘀 📊

• Creating databases and tables
• Inserting and selecting data
• Filtering records
• UPDATE table
• DELETE with WHERE
• ALTER TABLE operations
• DISTINCT, sorting, LIMIT, OFFSET
• Aggregations
• GROUP BY

#DBMS #SQL #Backend #ChaiCode
devwithjay's tweet image. 𝗗𝗮𝘁𝗮 𝘀𝗶𝗱𝗲 𝗯𝗲𝗴𝗶𝗻𝘀 📊

• Creating databases and tables
• Inserting and selecting data
• Filtering records
• UPDATE table
• DELETE with WHERE
• ALTER TABLE operations
• DISTINCT, sorting, LIMIT, OFFSET
• Aggregations
• GROUP BY

#DBMS #SQL #Backend #ChaiCode
devwithjay's tweet image. 𝗗𝗮𝘁𝗮 𝘀𝗶𝗱𝗲 𝗯𝗲𝗴𝗶𝗻𝘀 📊

• Creating databases and tables
• Inserting and selecting data
• Filtering records
• UPDATE table
• DELETE with WHERE
• ALTER TABLE operations
• DISTINCT, sorting, LIMIT, OFFSET
• Aggregations
• GROUP BY

#DBMS #SQL #Backend #ChaiCode

Designed a complete IPL management system from scratch today Started with ER diagram → entities, relations, junction tables Converted it into models and structured database properly Then moved to routes and actual backend flow #DBMS #ChaiCode @nirudhuuu @ChaiCodeHQ

rachit26227444's tweet image. Designed a complete IPL management system from scratch today  
Started with ER diagram → entities, relations, junction tables  
Converted it into models and structured database properly  
Then moved to routes and actual backend flow  
#DBMS #ChaiCode
@nirudhuuu @ChaiCodeHQ

𝗝𝗼𝗶𝗻𝘀 & 𝗜𝗻𝗱𝗲𝘅𝗶𝗻𝗴 ⚡ Today’s class went deeper into SQL, joins, foreign keys, & constraints. Also explored indexing and how databases optimize queries behind the scenes. Designed an Instagram-like system using ER diagrams. #DBMS #SQL #Backend #ChaiCode

devwithjay's tweet image. 𝗝𝗼𝗶𝗻𝘀 & 𝗜𝗻𝗱𝗲𝘅𝗶𝗻𝗴 ⚡

Today’s class went deeper into SQL, joins, foreign keys, & constraints.

Also explored indexing and how databases optimize queries behind the scenes.

Designed an Instagram-like system using ER diagrams.

#DBMS #SQL #Backend #ChaiCode
devwithjay's tweet image. 𝗝𝗼𝗶𝗻𝘀 & 𝗜𝗻𝗱𝗲𝘅𝗶𝗻𝗴 ⚡

Today’s class went deeper into SQL, joins, foreign keys, & constraints.

Also explored indexing and how databases optimize queries behind the scenes.

Designed an Instagram-like system using ER diagrams.

#DBMS #SQL #Backend #ChaiCode
devwithjay's tweet image. 𝗝𝗼𝗶𝗻𝘀 & 𝗜𝗻𝗱𝗲𝘅𝗶𝗻𝗴 ⚡

Today’s class went deeper into SQL, joins, foreign keys, & constraints.

Also explored indexing and how databases optimize queries behind the scenes.

Designed an Instagram-like system using ER diagrams.

#DBMS #SQL #Backend #ChaiCode

🚀 ER Diagram Assignment Done! Designed an Instagram-like thrift database — focused on structure, relationships & scalability. Grateful for the guidance 🙌 @Hiteshdotcom sir,@piyushgarg_dev @yntpdotme @nirudhuuu @surajtwt_ @devwithjay @ChaiCodeHQ #DBMS #SQL #LearningInPublic

tiwari_pee28336's tweet image. 🚀 ER Diagram Assignment Done!

Designed an Instagram-like thrift database — focused on structure, relationships & scalability.

Grateful for the guidance 🙌
@Hiteshdotcom sir,@piyushgarg_dev @yntpdotme @nirudhuuu @surajtwt_ @devwithjay @ChaiCodeHQ

#DBMS #SQL #LearningInPublic

The importance of Data, Data Access, and Data Connectivity has never been more evident than in the age of AI. This makes data access protocols like ODBC even more critical, as they empower AI Agents and their underlying frameworks to remain Database Management System (#DBMS)


Week 11 of my learning journey 🚀 Started **DBMS with Shubham Sir**. @Hiteshdotcom | @piyushgarg_dev |@nirudhuuu #DBMS #SQL #LearningJourney

RajSing62691380's tweet image. Week 11 of my learning journey 🚀

Started **DBMS with Shubham Sir**.
@Hiteshdotcom  | @piyushgarg_dev  |@nirudhuuu 

#DBMS #SQL #LearningJourney
RajSing62691380's tweet image. Week 11 of my learning journey 🚀

Started **DBMS with Shubham Sir**.
@Hiteshdotcom  | @piyushgarg_dev  |@nirudhuuu 

#DBMS #SQL #LearningJourney
RajSing62691380's tweet image. Week 11 of my learning journey 🚀

Started **DBMS with Shubham Sir**.
@Hiteshdotcom  | @piyushgarg_dev  |@nirudhuuu 

#DBMS #SQL #LearningJourney
RajSing62691380's tweet image. Week 11 of my learning journey 🚀

Started **DBMS with Shubham Sir**.
@Hiteshdotcom  | @piyushgarg_dev  |@nirudhuuu 

#DBMS #SQL #LearningJourney

Today I learned Dijkstra’s Algorithm — the shortest path approach for weighted graphs, and explored Bellman-Ford to handle negative edges. Also revised DBMS Sharding & Clustering — mastering data distribution & scalability. 🚀 #DSA #DBMS #Coding

UttamYadav_01's tweet image. Today I learned Dijkstra’s Algorithm — the shortest path approach for weighted graphs, and explored Bellman-Ford to handle negative edges.

Also revised DBMS Sharding & Clustering — mastering data distribution & scalability. 🚀

#DSA #DBMS #Coding
UttamYadav_01's tweet image. Today I learned Dijkstra’s Algorithm — the shortest path approach for weighted graphs, and explored Bellman-Ford to handle negative edges.

Also revised DBMS Sharding & Clustering — mastering data distribution & scalability. 🚀

#DSA #DBMS #Coding
UttamYadav_01's tweet image. Today I learned Dijkstra’s Algorithm — the shortest path approach for weighted graphs, and explored Bellman-Ford to handle negative edges.

Also revised DBMS Sharding & Clustering — mastering data distribution & scalability. 🚀

#DSA #DBMS #Coding

Built ER Diagram for a Clinic Diagnostics Platform: - Appointment vs. Consultation logic - Dynamic Test Catalog & Prescriptions - Snapshot pricing in Payments (No data corruption!) - Normalized Schema (13 Entities) @Hiteshdotcom @nirudhuuu @piyushgarg_dev #DBMS #ChaiCode

jai_baradia's tweet image. Built ER Diagram for a Clinic Diagnostics Platform:

- Appointment vs. Consultation logic 
- Dynamic Test Catalog & Prescriptions 
- Snapshot pricing in Payments (No data corruption!) 
- Normalized Schema (13 Entities)

@Hiteshdotcom @nirudhuuu @piyushgarg_dev 

#DBMS   #ChaiCode

【報道発表】アリババクラウド、ガートナー社の「Magic Quadrant™ for Cloud Database Management Systems」において6年連続で「リーダー」に選出 ⏩alibabacloud.com/blog/602747 自社開発 #DBMS 製品全般におけるサーバーレス対応など包括的なクラウドネイティブ機能や、#PolarDB

AlibabaCloud_jp's tweet image. 【報道発表】アリババクラウド、ガートナー社の「Magic Quadrant™ for Cloud Database Management Systems」において6年連続で「リーダー」に選出
⏩alibabacloud.com/blog/602747

自社開発 #DBMS 製品全般におけるサーバーレス対応など包括的なクラウドネイティブ機能や、#PolarDB

おはようございます。火曜日はスタバからスタート。経営情報システム2回目のトレーニングを解いています。 #中小企業診断士試験 #経営情報システム #DBMS #SQL #正規化

genlaishenshu's tweet image. おはようございます。火曜日はスタバからスタート。経営情報システム2回目のトレーニングを解いています。
#中小企業診断士試験
#経営情報システム
#DBMS
#SQL  
#正規化

Types: • Relational → tables + relationships • Non-relational → flexible, scalable Used everywhere from small businesses to social media apps. #Database #DBMS #TechBasics


Day 1 of #WinterArcChallenge ❄️ 📘 DSA: Learned Bellman-Ford algorithm revised Merge Two Linked Lists 💾 CS Fundamentals: Studied DBMS sharding, partitioning & scalable systems ⚡Revision: JavaScript basics Let’s keep pushing! 💪 #DSA #DBMS #SystemDesign #JavaScript #Coding

UttamYadav_01's tweet image. Day 1 of #WinterArcChallenge ❄️

📘 DSA:
Learned Bellman-Ford algorithm 
revised Merge Two Linked Lists

💾 CS Fundamentals:
Studied DBMS sharding, partitioning & scalable systems

⚡Revision: JavaScript basics
Let’s keep pushing! 💪
#DSA #DBMS #SystemDesign #JavaScript #Coding

L38 (29) inner join = where your data overlaps. only returns matching rows. select c.name, o.ordername from customer c inner join orders o on c.id=o.id; tip: "join" defaults to inner join! #DBMS #SQL #Databases

1drie5's tweet image. L38 (29)

inner join = where your data overlaps.
only returns matching rows.

select c.name, o.ordername
from customer c
inner join orders o on c.id=o.id;

tip: "join" defaults to inner join!

#DBMS #SQL #Databases

L38 (28) joins combine rows from tables via shared columns. inner = matched rows only. left/right = 1 side all + matches. full = everything from both. cross = cartesian. self = table to itself. fk not required — just matching data. #DBMS #SQL #Databases

1drie5's tweet image. L38 (28)

joins combine rows from tables via shared columns.

inner = matched rows only.
left/right = 1 side all + matches.
full = everything from both.
cross = cartesian.
self = table to itself.

fk not required — just matching data.

#DBMS #SQL #Databases


L38 (28) joins combine rows from tables via shared columns. inner = matched rows only. left/right = 1 side all + matches. full = everything from both. cross = cartesian. self = table to itself. fk not required — just matching data. #DBMS #SQL #Databases

1drie5's tweet image. L38 (28)

joins combine rows from tables via shared columns.

inner = matched rows only.
left/right = 1 side all + matches.
full = everything from both.
cross = cartesian.
self = table to itself.

fk not required — just matching data.

#DBMS #SQL #Databases

L38 (27) the order of sql commands: how you WRITE it: 1. select 2. from 3. where 4. group by 5. having 6. order by 7. limit how the database EXECUTES it: from → where → group by → having → select → order by → limit know the difference! #DBMS #SQL #Databases

1drie5's tweet image. L38 (27)

the order of sql commands:

how you WRITE it:
1. select
2. from
3. where
4. group by
5. having
6. order by
7. limit

how the database EXECUTES it:
from → where → group by → having → select → order by → limit

know the difference!

#DBMS #SQL #Databases


L38 (31) right join = all right + matches (else NULL). select c.name, o.ordername from customer c right join orders o on c.id = o.id; tip: rarely used! devs prefer flipping tables & using left joins. #DBMS #SQL #Databases

1drie5's tweet image. L38 (31)

right join = all right + matches (else NULL).

select c.name, o.ordername
from customer c
right join orders o
on c.id = o.id;

tip: rarely used! devs prefer flipping tables & using left joins.

#DBMS #SQL #Databases

L38 (30) left join = all left + matches (else NULL) select c.name from customer c left join orders o on c.id=o.id where is null; tip: is null → 0 buys #DBMS #SQL #Databases

1drie5's tweet image. L38 (30)

left join = all left + matches (else NULL)

select c.name
from customer c
left join orders o on c.id=o.id
where  is null;

tip:  is null → 0 buys

#DBMS #SQL #Databases


L38 (34) self join = table joined to itself. rule: MUST use aliases to separate rows! select m.name as mentor, s.name as student from student m join student s on m.s_id = s.mentor_id; tip: best for hierarchical data. #DBMS #SQL #Databases

1drie5's tweet image. L38 (34)

self join = table joined to itself.

rule: MUST use aliases to separate rows!

select m.name as mentor, s.name as student
from student m 
join student s on m.s_id = s.mentor_id;

tip: best for hierarchical data.

#DBMS #SQL #Databases

L38 (33) cross join = the cartesian product. every row from table A pairs with every row from B (m * n). select * from cust cross join ord; tip: careful! 1,000 users × 1,000 orders = 1 million rows. mostly used to generate test data or item variations. #DBMS #SQL #Databases

1drie5's tweet image. L38 (33)

cross join = the cartesian product.
every row from table A pairs with every row from B (m * n).

select * from cust cross join ord;

tip: careful! 1,000 users × 1,000 orders = 1 million rows. mostly used to generate test data or item variations.

#DBMS #SQL #Databases


L38 (33) cross join = the cartesian product. every row from table A pairs with every row from B (m * n). select * from cust cross join ord; tip: careful! 1,000 users × 1,000 orders = 1 million rows. mostly used to generate test data or item variations. #DBMS #SQL #Databases

1drie5's tweet image. L38 (33)

cross join = the cartesian product.
every row from table A pairs with every row from B (m * n).

select * from cust cross join ord;

tip: careful! 1,000 users × 1,000 orders = 1 million rows. mostly used to generate test data or item variations.

#DBMS #SQL #Databases

L38(32) full join = everything from both. MySQL lacks it! use UNION: select * from cust c left join ord o on c.id=o.id union select * from cust c right join ord o on =; #DBMS #SQL #Databases

1drie5's tweet image. L38(32)

full join = everything from both.

MySQL lacks it! use UNION:

select * from cust c left join ord o on c.id=o.id
union
select * from cust c right join ord o on =;

#DBMS #SQL #Databases


It's Finally Happening! Join us on 30th April for Live Keynotes, Dev tools and gain access to the most premium #cloud and #dbms system. Book a place: vasuki.cloud/io #x #vasukiitech #io #livekeynotes #devtools


L38(32) full join = everything from both. MySQL lacks it! use UNION: select * from cust c left join ord o on c.id=o.id union select * from cust c right join ord o on =; #DBMS #SQL #Databases

1drie5's tweet image. L38(32)

full join = everything from both.

MySQL lacks it! use UNION:

select * from cust c left join ord o on c.id=o.id
union
select * from cust c right join ord o on =;

#DBMS #SQL #Databases

L38 (31) right join = all right + matches (else NULL). select c.name, o.ordername from customer c right join orders o on c.id = o.id; tip: rarely used! devs prefer flipping tables & using left joins. #DBMS #SQL #Databases

1drie5's tweet image. L38 (31)

right join = all right + matches (else NULL).

select c.name, o.ordername
from customer c
right join orders o
on c.id = o.id;

tip: rarely used! devs prefer flipping tables & using left joins.

#DBMS #SQL #Databases


✨ From Kernel to Query—powering future tech minds! Student Development Program @ SRS FGC 🎓 Sessions on OS, Shell & DBMS by Dr. Nagesh B S (HoD, MCA, RNSIT) 💻📊 #StudentDevelopment #DBMS #OS #RNSIT

rnsitmca8421's tweet image. ✨ From Kernel to Query—powering future tech minds!
Student Development Program @ SRS FGC 🎓
Sessions on OS, Shell & DBMS by Dr. Nagesh B S (HoD, MCA, RNSIT) 💻📊
#StudentDevelopment #DBMS #OS #RNSIT
rnsitmca8421's tweet image. ✨ From Kernel to Query—powering future tech minds!
Student Development Program @ SRS FGC 🎓
Sessions on OS, Shell & DBMS by Dr. Nagesh B S (HoD, MCA, RNSIT) 💻📊
#StudentDevelopment #DBMS #OS #RNSIT
rnsitmca8421's tweet image. ✨ From Kernel to Query—powering future tech minds!
Student Development Program @ SRS FGC 🎓
Sessions on OS, Shell & DBMS by Dr. Nagesh B S (HoD, MCA, RNSIT) 💻📊
#StudentDevelopment #DBMS #OS #RNSIT
rnsitmca8421's tweet image. ✨ From Kernel to Query—powering future tech minds!
Student Development Program @ SRS FGC 🎓
Sessions on OS, Shell & DBMS by Dr. Nagesh B S (HoD, MCA, RNSIT) 💻📊
#StudentDevelopment #DBMS #OS #RNSIT

L38 (31) right join = all right + matches (else NULL). select c.name, o.ordername from customer c right join orders o on c.id = o.id; tip: rarely used! devs prefer flipping tables & using left joins. #DBMS #SQL #Databases

1drie5's tweet image. L38 (31)

right join = all right + matches (else NULL).

select c.name, o.ordername
from customer c
right join orders o
on c.id = o.id;

tip: rarely used! devs prefer flipping tables & using left joins.

#DBMS #SQL #Databases

L38 (30) left join = all left + matches (else NULL) select c.name from customer c left join orders o on c.id=o.id where is null; tip: is null → 0 buys #DBMS #SQL #Databases

1drie5's tweet image. L38 (30)

left join = all left + matches (else NULL)

select c.name
from customer c
left join orders o on c.id=o.id
where  is null;

tip:  is null → 0 buys

#DBMS #SQL #Databases


🚀Ready to crack your SQL Server interview? Start learning the real Interview Questions Series asked in top companies. Visit our channel now and level up your skills! youtube.com/shorts/z_lMsIV… #dbms #Data #sql #sqldatabase #sqldba #DBA #DBAProgram #madesimplemssql #Analyticss

MadeSimpleMSSQL's tweet card. SQL Server Interview Questions Part - 4 | #sql #education #coding |...

youtube.com

YouTube

SQL Server Interview Questions Part - 4 | #sql #education #coding |...


L38 (30) left join = all left + matches (else NULL) select c.name from customer c left join orders o on c.id=o.id where is null; tip: is null → 0 buys #DBMS #SQL #Databases

1drie5's tweet image. L38 (30)

left join = all left + matches (else NULL)

select c.name
from customer c
left join orders o on c.id=o.id
where  is null;

tip:  is null → 0 buys

#DBMS #SQL #Databases

L38 (29) inner join = where your data overlaps. only returns matching rows. select c.name, o.ordername from customer c inner join orders o on c.id=o.id; tip: "join" defaults to inner join! #DBMS #SQL #Databases

1drie5's tweet image. L38 (29)

inner join = where your data overlaps.
only returns matching rows.

select c.name, o.ordername
from customer c
inner join orders o on c.id=o.id;

tip: "join" defaults to inner join!

#DBMS #SQL #Databases


Why did embedded world 2026 honor eXtremeDB/rt with the embedded award: Software? #eXtremeDB/rt is the only COTS hard real time #DBMS. It also eliminates the flash file system and reducing write amplification. Learn more t.ly/jnUSB #EmbeddedSystems #flashmemory

RealTimeDBMS's tweet image. Why did embedded world 2026 honor eXtremeDB/rt with the embedded award: Software? 

#eXtremeDB/rt is the only COTS hard real time #DBMS.

It also eliminates the flash file system and reducing write amplification.

Learn more t.ly/jnUSB

#EmbeddedSystems #flashmemory

L38 (29) inner join = where your data overlaps. only returns matching rows. select c.name, o.ordername from customer c inner join orders o on c.id=o.id; tip: "join" defaults to inner join! #DBMS #SQL #Databases

1drie5's tweet image. L38 (29)

inner join = where your data overlaps.
only returns matching rows.

select c.name, o.ordername
from customer c
inner join orders o on c.id=o.id;

tip: "join" defaults to inner join!

#DBMS #SQL #Databases

L38 (28) joins combine rows from tables via shared columns. inner = matched rows only. left/right = 1 side all + matches. full = everything from both. cross = cartesian. self = table to itself. fk not required — just matching data. #DBMS #SQL #Databases

1drie5's tweet image. L38 (28)

joins combine rows from tables via shared columns.

inner = matched rows only.
left/right = 1 side all + matches.
full = everything from both.
cross = cartesian.
self = table to itself.

fk not required — just matching data.

#DBMS #SQL #Databases


Starting DBMS playlist by CodeHelp... #CodeHelp #DBMS

Rohan_269's tweet image. Starting DBMS playlist by CodeHelp...
#CodeHelp #DBMS

Week 12 Sunday Class From ERD → DB Models → API Built a full IPL backend flow and finally saw how design impacts everything Good schema = clean backend Bad schema = struggle This one clicked 💡 #Backend #SystemDesign #DBMS #chaicode @surajtwt_

Shriyansh_0210's tweet image. Week 12 Sunday Class 
From ERD → DB Models → API
Built a full IPL backend flow and finally saw how design impacts everything

Good schema = clean backend
Bad schema = struggle
This one clicked 💡

#Backend #SystemDesign #DBMS #chaicode
@surajtwt_
Shriyansh_0210's tweet image. Week 12 Sunday Class 
From ERD → DB Models → API
Built a full IPL backend flow and finally saw how design impacts everything

Good schema = clean backend
Bad schema = struggle
This one clicked 💡

#Backend #SystemDesign #DBMS #chaicode
@surajtwt_

Built a full IPL management system from scratch, starting with DataBase design, converting it into clean database models, then implementing routes and connecting the complete backend flow . @surajtwt_ @ChaiCodeHQ @nirudhuuu #Database #DBMS #ChaiCode #BackendDevelopment #ERD #API

_kumar_pratham's tweet image. Built a full IPL management system from scratch, starting with DataBase design, converting it into clean database models, then implementing routes and connecting the complete backend flow . @surajtwt_ @ChaiCodeHQ @nirudhuuu
#Database #DBMS #ChaiCode #BackendDevelopment #ERD #API
_kumar_pratham's tweet image. Built a full IPL management system from scratch, starting with DataBase design, converting it into clean database models, then implementing routes and connecting the complete backend flow . @surajtwt_ @ChaiCodeHQ @nirudhuuu
#Database #DBMS #ChaiCode #BackendDevelopment #ERD #API

Today’s cohort session → IPL management system ERD → entities, relations, embedding/references, junction tables → models + DB structuring → routes + backend flow from diagram → working backend #chaicode #backend #DBMS

Coder_dnano's tweet image. Today’s cohort session → IPL management system

ERD → entities, relations, embedding/references, junction tables
→ models + DB structuring
→ routes + backend flow

from diagram → working backend

#chaicode #backend #DBMS
Coder_dnano's tweet image. Today’s cohort session → IPL management system

ERD → entities, relations, embedding/references, junction tables
→ models + DB structuring
→ routes + backend flow

from diagram → working backend

#chaicode #backend #DBMS
Coder_dnano's tweet image. Today’s cohort session → IPL management system

ERD → entities, relations, embedding/references, junction tables
→ models + DB structuring
→ routes + backend flow

from diagram → working backend

#chaicode #backend #DBMS

L38 (28) joins combine rows from tables via shared columns. inner = matched rows only. left/right = 1 side all + matches. full = everything from both. cross = cartesian. self = table to itself. fk not required — just matching data. #DBMS #SQL #Databases

1drie5's tweet image. L38 (28)

joins combine rows from tables via shared columns.

inner = matched rows only.
left/right = 1 side all + matches.
full = everything from both.
cross = cartesian.
self = table to itself.

fk not required — just matching data.

#DBMS #SQL #Databases

L38 (27) the order of sql commands: how you WRITE it: 1. select 2. from 3. where 4. group by 5. having 6. order by 7. limit how the database EXECUTES it: from → where → group by → having → select → order by → limit know the difference! #DBMS #SQL #Databases

1drie5's tweet image. L38 (27)

the order of sql commands:

how you WRITE it:
1. select
2. from
3. where
4. group by
5. having
6. order by
7. limit

how the database EXECUTES it:
from → where → group by → having → select → order by → limit

know the difference!

#DBMS #SQL #Databases


In today's class we designed a complete IPL management system from scratch. Started with ER diagram - entities, relations, junction tables. Converted it into models and structured database properly. Then moved to routes and actual backend flow @nirudhuuu #DBMS #ChaiCode

BhavayNagpal1's tweet image. In today's class we designed a complete IPL management system from scratch.
Started with ER diagram - entities, relations, junction tables.
Converted it into models and structured database properly.
Then moved to routes and actual backend flow

@nirudhuuu

#DBMS #ChaiCode
BhavayNagpal1's tweet image. In today's class we designed a complete IPL management system from scratch.
Started with ER diagram - entities, relations, junction tables.
Converted it into models and structured database properly.
Then moved to routes and actual backend flow

@nirudhuuu

#DBMS #ChaiCode

Designed a complete IPL management system from scratch today Started with ER diagram → entities, relations, junction tables Converted it into models and structured database properly Then moved to routes and actual backend flow #DBMS #ChaiCode @nirudhuuu @ChaiCodeHQ

rachit26227444's tweet image. Designed a complete IPL management system from scratch today  
Started with ER diagram → entities, relations, junction tables  
Converted it into models and structured database properly  
Then moved to routes and actual backend flow  
#DBMS #ChaiCode
@nirudhuuu @ChaiCodeHQ

Week 11 of my learning journey 🚀 Started **DBMS with Shubham Sir**. @Hiteshdotcom | @piyushgarg_dev |@nirudhuuu #DBMS #SQL #LearningJourney

RajSing62691380's tweet image. Week 11 of my learning journey 🚀

Started **DBMS with Shubham Sir**.
@Hiteshdotcom  | @piyushgarg_dev  |@nirudhuuu 

#DBMS #SQL #LearningJourney
RajSing62691380's tweet image. Week 11 of my learning journey 🚀

Started **DBMS with Shubham Sir**.
@Hiteshdotcom  | @piyushgarg_dev  |@nirudhuuu 

#DBMS #SQL #LearningJourney
RajSing62691380's tweet image. Week 11 of my learning journey 🚀

Started **DBMS with Shubham Sir**.
@Hiteshdotcom  | @piyushgarg_dev  |@nirudhuuu 

#DBMS #SQL #LearningJourney
RajSing62691380's tweet image. Week 11 of my learning journey 🚀

Started **DBMS with Shubham Sir**.
@Hiteshdotcom  | @piyushgarg_dev  |@nirudhuuu 

#DBMS #SQL #LearningJourney

𝗗𝗮𝘁𝗮 𝘀𝗶𝗱𝗲 𝗯𝗲𝗴𝗶𝗻𝘀 📊 • Creating databases and tables • Inserting and selecting data • Filtering records • UPDATE table • DELETE with WHERE • ALTER TABLE operations • DISTINCT, sorting, LIMIT, OFFSET • Aggregations • GROUP BY #DBMS #SQL #Backend #ChaiCode

devwithjay's tweet image. 𝗗𝗮𝘁𝗮 𝘀𝗶𝗱𝗲 𝗯𝗲𝗴𝗶𝗻𝘀 📊

• Creating databases and tables
• Inserting and selecting data
• Filtering records
• UPDATE table
• DELETE with WHERE
• ALTER TABLE operations
• DISTINCT, sorting, LIMIT, OFFSET
• Aggregations
• GROUP BY

#DBMS #SQL #Backend #ChaiCode
devwithjay's tweet image. 𝗗𝗮𝘁𝗮 𝘀𝗶𝗱𝗲 𝗯𝗲𝗴𝗶𝗻𝘀 📊

• Creating databases and tables
• Inserting and selecting data
• Filtering records
• UPDATE table
• DELETE with WHERE
• ALTER TABLE operations
• DISTINCT, sorting, LIMIT, OFFSET
• Aggregations
• GROUP BY

#DBMS #SQL #Backend #ChaiCode
devwithjay's tweet image. 𝗗𝗮𝘁𝗮 𝘀𝗶𝗱𝗲 𝗯𝗲𝗴𝗶𝗻𝘀 📊

• Creating databases and tables
• Inserting and selecting data
• Filtering records
• UPDATE table
• DELETE with WHERE
• ALTER TABLE operations
• DISTINCT, sorting, LIMIT, OFFSET
• Aggregations
• GROUP BY

#DBMS #SQL #Backend #ChaiCode

𝗙𝗿𝗼𝗺 𝗘𝗥𝗗 𝘁𝗼 𝗔𝗣𝗜 🏏 Designed a complete IPL management system from scratch today Started with ER diagram → entities, relations, junction tables Converted it into models and structured database properly Then moved to routes and actual backend flow #DBMS #ChaiCode

devwithjay's tweet image. 𝗙𝗿𝗼𝗺 𝗘𝗥𝗗 𝘁𝗼 𝗔𝗣𝗜 🏏

Designed a complete IPL management system from scratch today

Started with ER diagram → entities, relations, junction tables

Converted it into models and structured database properly

Then moved to routes and actual backend flow

#DBMS #ChaiCode
devwithjay's tweet image. 𝗙𝗿𝗼𝗺 𝗘𝗥𝗗 𝘁𝗼 𝗔𝗣𝗜 🏏

Designed a complete IPL management system from scratch today

Started with ER diagram → entities, relations, junction tables

Converted it into models and structured database properly

Then moved to routes and actual backend flow

#DBMS #ChaiCode
devwithjay's tweet image. 𝗙𝗿𝗼𝗺 𝗘𝗥𝗗 𝘁𝗼 𝗔𝗣𝗜 🏏

Designed a complete IPL management system from scratch today

Started with ER diagram → entities, relations, junction tables

Converted it into models and structured database properly

Then moved to routes and actual backend flow

#DBMS #ChaiCode

𝗝𝗼𝗶𝗻𝘀 & 𝗜𝗻𝗱𝗲𝘅𝗶𝗻𝗴 ⚡ Today’s class went deeper into SQL, joins, foreign keys, & constraints. Also explored indexing and how databases optimize queries behind the scenes. Designed an Instagram-like system using ER diagrams. #DBMS #SQL #Backend #ChaiCode

devwithjay's tweet image. 𝗝𝗼𝗶𝗻𝘀 & 𝗜𝗻𝗱𝗲𝘅𝗶𝗻𝗴 ⚡

Today’s class went deeper into SQL, joins, foreign keys, & constraints.

Also explored indexing and how databases optimize queries behind the scenes.

Designed an Instagram-like system using ER diagrams.

#DBMS #SQL #Backend #ChaiCode
devwithjay's tweet image. 𝗝𝗼𝗶𝗻𝘀 & 𝗜𝗻𝗱𝗲𝘅𝗶𝗻𝗴 ⚡

Today’s class went deeper into SQL, joins, foreign keys, & constraints.

Also explored indexing and how databases optimize queries behind the scenes.

Designed an Instagram-like system using ER diagrams.

#DBMS #SQL #Backend #ChaiCode
devwithjay's tweet image. 𝗝𝗼𝗶𝗻𝘀 & 𝗜𝗻𝗱𝗲𝘅𝗶𝗻𝗴 ⚡

Today’s class went deeper into SQL, joins, foreign keys, & constraints.

Also explored indexing and how databases optimize queries behind the scenes.

Designed an Instagram-like system using ER diagrams.

#DBMS #SQL #Backend #ChaiCode

What are counterfeit Knowledge Graphs? Please read: linkedin.com/pulse/counterf… #KnowledgeGraph has become a confusing term that confuses unsuspecting end-users and integrators. Even more so in the age of #GenAI. #DBMS #LPG #RDBMS #Issues #CDO #CIO #CAIO #CMO #AI #RAG #GraphRAG

kidehen's tweet image. What are counterfeit Knowledge Graphs? Please read: linkedin.com/pulse/counterf… 

#KnowledgeGraph has become a confusing term that confuses unsuspecting end-users and integrators. Even more so in the age of #GenAI. 

#DBMS #LPG #RDBMS #Issues #CDO #CIO #CAIO #CMO #AI #RAG #GraphRAG

Day 1 of #WinterArcChallenge ❄️ 📘 DSA: Learned Bellman-Ford algorithm revised Merge Two Linked Lists 💾 CS Fundamentals: Studied DBMS sharding, partitioning & scalable systems ⚡Revision: JavaScript basics Let’s keep pushing! 💪 #DSA #DBMS #SystemDesign #JavaScript #Coding

UttamYadav_01's tweet image. Day 1 of #WinterArcChallenge ❄️

📘 DSA:
Learned Bellman-Ford algorithm 
revised Merge Two Linked Lists

💾 CS Fundamentals:
Studied DBMS sharding, partitioning & scalable systems

⚡Revision: JavaScript basics
Let’s keep pushing! 💪
#DSA #DBMS #SystemDesign #JavaScript #Coding

🚀 ER Diagram Assignment Done! Designed an Instagram-like thrift database — focused on structure, relationships & scalability. Grateful for the guidance 🙌 @Hiteshdotcom sir,@piyushgarg_dev @yntpdotme @nirudhuuu @surajtwt_ @devwithjay @ChaiCodeHQ #DBMS #SQL #LearningInPublic

tiwari_pee28336's tweet image. 🚀 ER Diagram Assignment Done!

Designed an Instagram-like thrift database — focused on structure, relationships & scalability.

Grateful for the guidance 🙌
@Hiteshdotcom sir,@piyushgarg_dev @yntpdotme @nirudhuuu @surajtwt_ @devwithjay @ChaiCodeHQ

#DBMS #SQL #LearningInPublic

Read the insightful new article "Engineering Real-Time: Lessons Learned While Chasing Determinism" part 1 in @embedded_comp t.ly/oJbaX #realtimedata #DBMS #deterministicdatabase

RealTimeDBMS's tweet image. Read the insightful new article "Engineering Real-Time: Lessons Learned While Chasing Determinism" part 1 in @embedded_comp t.ly/oJbaX

#realtimedata #DBMS #deterministicdatabase

IPL mangement DB Design done @surajtwt_ sir and @nirudhuuu sir was thinking to go deep but as said to make a simple desing so here it is. #DBMS #chaicode #Backend

jai_baradia's tweet image. IPL mangement DB Design done @surajtwt_  sir and @nirudhuuu sir

was thinking to go deep but as said to make a simple desing so here it is.

#DBMS #chaicode #Backend

おはようございます。火曜日はスタバからスタート。経営情報システム2回目のトレーニングを解いています。 #中小企業診断士試験 #経営情報システム #DBMS #SQL #正規化

genlaishenshu's tweet image. おはようございます。火曜日はスタバからスタート。経営情報システム2回目のトレーニングを解いています。
#中小企業診断士試験
#経営情報システム
#DBMS
#SQL  
#正規化

【報道発表】アリババクラウド、ガートナー社の「Magic Quadrant™ for Cloud Database Management Systems」において6年連続で「リーダー」に選出 ⏩alibabacloud.com/blog/602747 自社開発 #DBMS 製品全般におけるサーバーレス対応など包括的なクラウドネイティブ機能や、#PolarDB

AlibabaCloud_jp's tweet image. 【報道発表】アリババクラウド、ガートナー社の「Magic Quadrant™ for Cloud Database Management Systems」において6年連続で「リーダー」に選出
⏩alibabacloud.com/blog/602747

自社開発 #DBMS 製品全般におけるサーバーレス対応など包括的なクラウドネイティブ機能や、#PolarDB

Why did embedded world 2026 honor eXtremeDB/rt with the embedded award: Software? #eXtremeDB/rt is the only COTS hard real time #DBMS. It also eliminates the flash file system and reducing write amplification. Learn more t.ly/jnUSB #EmbeddedSystems #flashmemory

RealTimeDBMS's tweet image. Why did embedded world 2026 honor eXtremeDB/rt with the embedded award: Software? 

#eXtremeDB/rt is the only COTS hard real time #DBMS.

It also eliminates the flash file system and reducing write amplification.

Learn more t.ly/jnUSB

#EmbeddedSystems #flashmemory

We invite you to review our research & articles about *hard* real-time #DBMS. If you believe that something on this page needs to be changed, or if you have something to add, please let us know in the link on the page. t.ly/1HqqO #RealTimeData #RTOS #EmbeddedSystems

RealTimeDBMS's tweet image. We invite you to review our research & articles about *hard* real-time #DBMS. If you believe that something on this page needs to be changed, or if you have something to add, please let us know in the link on the page.
t.ly/1HqqO

#RealTimeData #RTOS #EmbeddedSystems

𝗗𝗮𝘁𝗮 𝘀𝗶𝗱𝗲 𝗯𝗲𝗴𝗶𝗻𝘀 📊 • Creating databases and tables • Inserting and selecting data • Filtering records • UPDATE table • DELETE with WHERE • ALTER TABLE operations • DISTINCT, sorting, LIMIT, OFFSET • Aggregations • GROUP BY #DBMS #SQL #Backend #ChaiCode

Jatin_17824's tweet image. 𝗗𝗮𝘁𝗮 𝘀𝗶𝗱𝗲 𝗯𝗲𝗴𝗶𝗻𝘀 📊
• Creating databases and tables
• Inserting and selecting data
• Filtering records
• UPDATE table
• DELETE with WHERE
• ALTER TABLE operations
• DISTINCT, sorting, LIMIT, OFFSET
• Aggregations
• GROUP BY  

#DBMS #SQL #Backend #ChaiCode
Jatin_17824's tweet image. 𝗗𝗮𝘁𝗮 𝘀𝗶𝗱𝗲 𝗯𝗲𝗴𝗶𝗻𝘀 📊
• Creating databases and tables
• Inserting and selecting data
• Filtering records
• UPDATE table
• DELETE with WHERE
• ALTER TABLE operations
• DISTINCT, sorting, LIMIT, OFFSET
• Aggregations
• GROUP BY  

#DBMS #SQL #Backend #ChaiCode
Jatin_17824's tweet image. 𝗗𝗮𝘁𝗮 𝘀𝗶𝗱𝗲 𝗯𝗲𝗴𝗶𝗻𝘀 📊
• Creating databases and tables
• Inserting and selecting data
• Filtering records
• UPDATE table
• DELETE with WHERE
• ALTER TABLE operations
• DISTINCT, sorting, LIMIT, OFFSET
• Aggregations
• GROUP BY  

#DBMS #SQL #Backend #ChaiCode

Today I learned Dijkstra’s Algorithm — the shortest path approach for weighted graphs, and explored Bellman-Ford to handle negative edges. Also revised DBMS Sharding & Clustering — mastering data distribution & scalability. 🚀 #DSA #DBMS #Coding

UttamYadav_01's tweet image. Today I learned Dijkstra’s Algorithm — the shortest path approach for weighted graphs, and explored Bellman-Ford to handle negative edges.

Also revised DBMS Sharding & Clustering — mastering data distribution & scalability. 🚀

#DSA #DBMS #Coding
UttamYadav_01's tweet image. Today I learned Dijkstra’s Algorithm — the shortest path approach for weighted graphs, and explored Bellman-Ford to handle negative edges.

Also revised DBMS Sharding & Clustering — mastering data distribution & scalability. 🚀

#DSA #DBMS #Coding
UttamYadav_01's tweet image. Today I learned Dijkstra’s Algorithm — the shortest path approach for weighted graphs, and explored Bellman-Ford to handle negative edges.

Also revised DBMS Sharding & Clustering — mastering data distribution & scalability. 🚀

#DSA #DBMS #Coding

L38 (23) aggregate functions = summarize rows into 1 value. > count() — total rows > sum() — column total > avg() — average > min() — smallest > max() — largest select count(*), sum(salary), avg(salary), min(salary), max(salary) from employees; #DBMS #SQL #Databases

1drie5's tweet image. L38 (23)

aggregate functions = summarize rows into 1 value.
> count() — total rows
> sum() — column total
> avg() — average
> min() — smallest
> max() — largest

select count(*), sum(salary), avg(salary), min(salary), max(salary) from employees;

#DBMS #SQL #Databases

L38 (22) sql clauses control data flow: > where = filter rows. select * from emp where age > 20; > order by = sort (asc/desc). select * from emp order by salary desc; > limit = restrict count. select * from emp limit 5; combine to find "top n" records! #DBMS #SQL #Databases

1drie5's tweet image. L38 (22)

sql clauses control data flow:

> where = filter rows.
select * from emp where age > 20;

> order by = sort (asc/desc).
select * from emp order by salary desc;

> limit = restrict count.
select * from emp limit 5;

combine to find "top n" records!

#DBMS #SQL #Databases


Starting my DBMS learning journey today. 📚 Let’s see how much time it takes me to complete this video by @lovebabbar3 and understand the core concepts properly. Consistency mode: ON 🚀 #DBMS #LearningInPublic #100DaysOfCode #CSStudents

SaahilxRajput's tweet image. Starting my DBMS learning journey today. 📚

Let’s see how much time it takes me to complete this video by @lovebabbar3 and understand the core concepts properly.

Consistency mode: ON 🚀

#DBMS #LearningInPublic #100DaysOfCode #CSStudents

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