Knowing the top databases in the world has never been more important. Every developer, analyst, or tech beginner needs this knowledge before stepping into 2025.
I’ve seen careers skyrocket because someone mastered the right database at the right time — and I’ve watched startups waste months of engineering effort because they picked the wrong one.
2025 is fascinating because the database landscape is shifting faster than ever. PostgreSQL keeps climbing the DB-Engines rankings, MongoDB adoption is exploding thanks to its cloud-native ecosystem, and MySQL and SQL remains the rock-solid backbone of millions of web apps. Today, nearly 80% of companies run both SQL and NoSQL databases side by side to handle structured, semi-structured, and real-time data.
That means modern developers need more than basic SQL — they need the ability to choose the right tool for the job.
– Social app with rapidly changing data → MongoDB for flexible JSON?
– AI recommendation engine → Neo4j for complex relationships?
– Fintech where every penny must be correct → PostgreSQL for bulletproof ACID?
These decisions directly affect performance, scalability, cost, and user trust.
Here’s the truth: 2025 isn’t SQL vs NoSQL anymore.
It’s SQL and NoSQL — together.
In this guide, you’ll get a clear breakdown of database types, SQL vs NoSQL realities, real-world use cases, and the logic behind choices like PostgreSQL vs MySQL, MongoDB vs SQL, Redis vs MongoDB, Cassandra vs MongoDB, and more.
The goal? Help you understand how modern systems really work — and give your career the boost it deserves in 2025.
Let’s make this the year you master the right database skills. 🚀
Key Highlights – A Quick Snapshot
- ⭐ The top databases in the world to learn before 2025 — with real engineering insights
- ⭐ Simple breakdown of the types of databases (SQL + NoSQL)
- ⭐ Clear explanations of SQL vs NoSQL — without jargon
- ⭐ Practical differences: PostgreSQL vs MySQL, MongoDB vs SQL, Redis vs MongoDB, MongoDB vs Cassandra, SQLite vs MySQL
- ⭐ When developers actually choose PostgreSQL over MySQL — and why
- ⭐ Real-world use cases from fintech, AI, social apps, gaming, and SaaS
- ⭐ The mistake many beginners make when choosing a database
- ⭐ The fastest-growing database skills companies want in 2025
- ⭐ A simple learning roadmap to master SQL and NoSQL side by side
Understanding the Types of Databases – SQL + NoSQL Explained Simply
Most people start learning databases without understanding the foundation — and that’s exactly why they get confused later. So let’s break this down in a way that actually makes sense.
SQL Databases – Structured, Reliable, ACID-Strong
These are the traditional, rock-solid systems built around tables, rows, and strict structure.
Popular SQL databases include PostgreSQL, MySQL, Oracle, SQLite, and Microsoft SQL Server Management Studio.
SQL shines when:
- Data is predictable
- Accuracy matters
- You need strong consistency
- You want powerful joins and relationships
Real Example:
A fintech app that tracks transactions cannot afford even a 1-paisā mismatch.
→ SQL databases like PostgreSQL or MySQL are the default choice.
NoSQL Databases (Flexible, Fast, Scalable)
NoSQL isn’t “one” type — it’s a family of models designed to handle modern, messy, fast-changing data.
Here’s the simplest way to understand them:
- MongoDB → Document database (stores JSON-like documents)
- Cassandra → Wide-column store for massive, distributed systems
- Redis → In-memory key-value store for speed-critical workloads
- Neo4j → Graph database for relationships and recommendations
- Firebase → Realtime serverless database for mobile and web apps
NoSQL shines when:
- Data structure keeps changing
- You’re storing events, JSON, logs, sessions
- You need insane speed or horizontal scaling
- You’re building mobile apps or social features
Real Example:
A chat application with millions of realtime messages?
→ MongoDB or Firebase handles it far better than old-school tables.
SQL vs NoSQL
Developers love this debate — but the truth is simple: both work, both matter, and both break if you use them wrong.
Let’s cut the noise and get into the real stuff.
MongoDB vs SQL
MongoDB gives flexibility, JSON storage, fast iteration.
SQL gives structure, accuracy, and reliability.
When MongoDB wins:
- Data keeps evolving
- You hate schema migrations
- You care more about speed than relationships
When SQL wins:
- You need strong consistency
- You have complex joins
- You want ACID-safe transactions
Difference Between SQL and MySQL
This confuses almost every beginner:
- SQL → a language
- MySQL → a database that uses SQL
Think of SQL as English… and MySQL as a book written in English.
Why:
Many engineers learn this the hard way:
A startup once picked MongoDB for a financial analytics tool because “MongoDB is trending.”
Six months later, they had inconsistent reports, broken aggregations, and nightmare queries.
They switched to PostgreSQL — problem solved.
Wrong tool. Wrong job. Big lesson.
Where SQL Breaks
- Real-time systems with unpredictable data
- Massive horizontal scaling
- Highly flexible JSON structures
Where NoSQL Breaks
- Multi-table joins
- Financial-grade accuracy
- Complex relational queries
- When developers treat a document DB like a relational DB
Best Database to Learn in 2025 Based on Your Career Path
There’s no “one best database” — there’s only the best database for the career you want.
Here’s the simplest, most practical breakdown you’ll ever see.
1) PostgreSQL
Open-source relational DBMS (ACID, advanced SQL features). Used for transactional systems, analytics, geospatial (PostGIS), and as a general purpose primary DB for web, mobile, and cloud apps.
What it is
A powerful open-source relational database (SQL-based).
Relational = data stored in tables like Excel sheets.
Where it’s used
Web apps, mobile apps, finance systems, analytics, and anything needing reliable transactions.
Advantages
- Very SQL-standard compliant and feature rich (JSONB, window functions, indexing types).
- Strong open-source community and tooling (extensions).
- Good for OLTP and analytic workloads with correct tuning.
Disadvantages
- Heavier to manage than some hosted NoSQL options for extreme scale (but many managed cloud offerings exist).
- Some enterprise features require extra tooling or commercial forks for specific needs.
Performance notes
- Excellent single-node performance for complex queries; scales vertically and via read-replicas / partitioning; distributed SQL alternatives (Citus, Yugabyte) used when horizontal scale is needed.
Usage / adoption (India & global)
- Stack Overflow developer survey (2024) shows PostgreSQL very widely used — reported by ~49% of developers in recent surveys, making it one of the top databases for developers. (Stack Overflow)
- Verified company adoption datasets show tens of thousands of companies using PostgreSQL (company usage databases list many thousands of deployments). (Landbase Data)
Career outlook (India & global)
- High demand for Postgres skills — many LinkedIn job postings worldwide and in India (LinkedIn shows 10k–14k+ postgres-tagged jobs in India at the time of checking). Typical roles: Backend engineer, Data engineer, DBA, Site Reliability Engineer. (LinkedIn)
Future-proof / career growth rating: 5/5 — versatile, open-source, widely adopted in modern cloud & enterprise stacks.
2) MySQL
Open-source (Oracle-backed) relational DBMS widely used for web apps, CMSs, e-commerce, and many LAMP-stack applications.
What it is
A very popular open-source SQL database used for websites and apps.
Where it’s used
E-commerce sites, WordPress, SaaS apps, CRMs, startups.
Advantages
- Extremely widespread and simple to get started with; mature ecosystem.
- Many managed cloud offerings (RDS/Aurora, Cloud SQL).
Disadvantages
- Fewer advanced features compared to Postgres (though modern MySQL/MariaDB have closed many gaps).
- Historically had fragmentation between MySQL, MariaDB, forks.
Performance notes
- Very good read performance; scales with replication and read-shards; write scaling requires sharding or cloud-native services.
Usage / adoption
- MySQL remains one of the largest installed relational engines — company dataset counts show MySQL in the hundreds of thousands of companies globally. One market data source reports MySQL used by ~190k companies in 2025. (6sense)
Career outlook (India & global)
- Very strong — many junior and mid roles expect MySQL knowledge. LinkedIn shows 14k+ MySQL jobs in India (broad demand for DBAs and developers). (LinkedIn)
Future-proof / career growth rating: 4/5 — ubiquity gives steady demand; stronger competition with Postgres in advanced SQL features.
3) MongoDB
Document-store NoSQL database (JSON-like BSON). Great for flexible schemas, content platforms, product catalogs, telemetry, and rapid iteration.
What it is
A NoSQL document database that stores data as JSON-like documents.
NoSQL = flexible, not table-based.
Where it’s used
Product catalogs, social apps, analytics, real-time apps.
Advantages
- Schema flexibility speeds development.
- Rich query language and aggregation framework.
- Strong managed/cloud offering (Atlas).
Disadvantages
- Not ACID across multi-document transactions historically (improved recently) — care required for transactional use cases.
- Can be misused for relational patterns leading to data duplication or consistency challenges.
Performance notes
- Fast for document reads/writes, heavy indexing required for query performance; scales horizontally via sharding.
Usage / adoption
- Large enterprise presence; vendor (MongoDB) continues high activity and customer stories; analyst coverage and stock/market interest remain high in 2025. (MongoDB)
Career outlook (India & global)
- Strong demand — LinkedIn shows ~13k MongoDB-tagged jobs in India; common in startups and cloud-native stacks. (LinkedIn)
Future-proof / career growth rating: 4/5 — excellent for modern app development; long term skills map well to many NoSQL stores and document-oriented patterns.
4) Redis
In-memory key-value store, used for caching, pub/sub, session stores, queues, and fast ephemeral data. Also used as a primary DB in low-latency scenarios.
What it is
A super-fast in-memory key-value store.
In-memory = data stored in RAM → extremely fast.
Where it’s used
Caching, chat apps, leaderboards, queues, AI retrieval, session storage.
Advantages
- Extremely low latency (sub-millisecond), simple data structures (strings, lists, sets, sorted sets, streams).
- Multi-use (cache, queue, session, leaderboard).
Disadvantages
- Memory cost for large data sets; persistence/backup patterns differ from disk-based databases.
- Licensing and commercial changes in recent years have caused community friction (some ecosystem shifts). (Medium)
Performance notes
- Best-in-class latency; throughput dependent on memory and network; used as complement to primary DBs.
Usage / adoption
- Widely used across companies for caching and RAG/LLM retrieval stacks (Redis blogs highlight use in RAG and fast retrieval). (Redis)
Career outlook (India & global)
- High demand for developers and SREs who know Redis, especially at scale or working with caching/real-time systems; Redis skills are often required alongside primary DB skills.
Future-proof / career growth rating: 5/5 for performance/real-time roles — Redis is likely to remain essential for low-latency and AI retrieval patterns.
5) Microsoft SQL Server
Enterprise relational DBMS from Microsoft. Widely used in corporate environments, ERP, finance, BI (SSIS/SSRS), and .NET stacks.
What it is
A powerful enterprise SQL database from Microsoft.
Where it’s used
Banks, enterprises, government, corporate systems, .NET applications.
Advantages
- Rich enterprise tooling, integrations with Microsoft stack, hardened security, analytics/BI capabilities.
- Strong on Windows and now cross-platform on Linux.
Disadvantages
- Licensing costs for enterprise editions (can be high).
- Less common in cloud-native open-source shops, but strong in enterprise IT.
Performance notes
- Excellent OLTP performance and strong tooling for tuning, in-memory OLTP options, columnstore for analytics.
Usage / adoption
- Microsoft released SQL Server 2025 (GA recently) — continued investment shows enterprise stability. (Microsoft Learn)
Career outlook (India & global)
- Very good in enterprises, finance, and large product companies that use Microsoft stacks; DBAs and BI engineers often require SQL Server expertise.
Future-proof / career growth rating: 4/5 — enterprise demand remains, especially where Microsoft stack is dominant.
6) Oracle Database
Legacy/enterprise relational DBMS widely used by large enterprises for mission-critical systems, OLTP, data warehouses.
What it is
A very mature and highly reliable enterprise SQL database used for mission-critical systems.
Where it’s used
Banking, telecom, airlines, government systems, massive enterprise workloads.
Advantages
- Extremely mature, robust for very large, mission-critical DBs; rich enterprise features and support.
- Large ecosystem of enterprise tools.
Disadvantages
- High licensing and TCO; steeper learning curve for modern cloud workflows (Oracle is investing in cloud but costs matter).
Performance notes
- Engineered for high throughput and strong availability at scale; used in large transactional systems.
Usage / adoption
- Still heavily used in large enterprises; Oracle’s cloud business and major contracts in 2025 underline ongoing enterprise traction. (Reuters)
Career outlook (India & global)
- Strong demand for experienced Oracle DBAs in large enterprises, banks, and government / legacy systems. Good compensation but roles are more specialized.
Future-proof / career growth rating: 3/5 — durable in legacy enterprise contexts, but growth may be slower vs. cloud/open-source options.
7) Elasticsearch / OpenSearch
Search engine / distributed analytics engine (full-text search, log analytics). OpenSearch is the community fork of Elasticsearch.
What it is
A search engine + analytics engine used for text search and log analysis.
Where it’s used
Search bars, logging systems (ELK), observability dashboards, e-commerce search.
Advantages
- Powerful full-text search, near real-time indexing, excellent for logs/observability (ELK/Elastic Stack).
- Fast for search and analytics queries across large text corpora.
Disadvantages
- Can be resource-hungry; operations/cluster management can be complex at scale.
- Recent licensing changes with Elasticsearch led to ecosystem split (OpenSearch). Choose based on licensing & vendor preferences.
Performance notes
- Excellent search latency; scales horizontally but needs careful tuning (shards, replicas).
Usage / adoption
- Widely used for log analytics, observability, and search across many companies and SaaS offerings.
Career outlook (India & global)
- High demand in SRE/Observability/DevOps and backend roles; experience with the Elastic Stack or OpenSearch is valuable.
Future-proof / career growth rating: 4/5 — indispensable where search or observability is needed.
8) Cassandra
Wide-column, distributed NoSQL DB designed for linear horizontal scale and high availability (eventual consistency). Popular for telemetry, time series, massive write workloads.
What it is
A distributed NoSQL wide-column database designed for massive scale.
Wide-column = data stored in a flexible table-like structure, good for large datasets.
Where it’s used
IoT, messaging systems, time-series data, telemetry, huge-scale apps.
Advantages
- Excellent write scalability, multi-region replication, high availability.
- Designed for large data volumes across many nodes.
Disadvantages
- Eventual consistency model requires different design patterns; harder query expressiveness vs. SQL.
- Operational complexity and tuning can be non-trivial.
Performance notes
- Outstanding for write-heavy, high-throughput workloads; read patterns must be designed with care.
Usage / adoption
- Used by high-scale services (telemetry, messaging, some social / ad platforms). Not as common in mainstream small apps but crucial in large scale systems.
Career outlook (India & global)
- High value in companies that ingest huge event streams — experience yields senior SRE/data engineering roles.
Future-proof / career growth rating: 4/5 — niche but critical for scale-oriented architectures.
9) Amazon DynamoDB
Fully managed key-value and document database (AWS). Used for serverless, high throughput, low-latency workloads where AWS-managed scaling is desired.
What it is
A fully managed NoSQL key-value + document database from AWS.
Where it’s used
Serverless apps, mobile backends, gaming, IoT, high-traffic apps.
Advantages
- No operational overhead (managed), predictable scale, integrates with AWS services and serverless paradigms.
- Strong for high-scale, low-latency use cases (mobile backends, gaming, IoT).
Disadvantages
- Pricing model and partitioning ergonomics (hot keys) require careful design.
- Query expressiveness limited compared to SQL; vendor lock-in to AWS.
Performance notes
- Very low latency at scale; throughput scales with provisioned or on-demand capacity.
Usage / adoption
- Widely used in AWS ecosystems and companies preferring managed serverless DBs.
Career outlook (India & global)
- Excellent for cloud-native roles — AWS certifications and hands-on DynamoDB experience are highly valued.
Future-proof / career growth rating: 4/5 — strong for cloud-native & serverless careers, but vendor lock-in tradeoffs.
10) SQLite
Embedded, file-based relational DB. Used inside mobile apps, desktop apps, browser extensions, small tools, and as a default light DB for many platforms.
What it is
A tiny file-based SQL database that runs inside apps.
Used in phones, browsers, desktop apps.
Where it’s used
Android apps, iOS apps, small apps, offline storage.
Advantages
- Extremely lightweight, zero-config, fast for local workloads, ubiquitous (used in mobile OSs, embedded devices).
- Great for prototyping and app-local storage.
Disadvantages
- Not suitable for high concurrency server workloads; limited scale compared to server RDBMS.
Performance notes
- Excellent for local single-writer workloads; scales well for embedded scenarios.
Usage / adoption
- Ubiquitous on devices and offline apps (practically every mobile app uses SQLite or similar embedded DB for local storage).
Career outlook (India & global)
- Useful for mobile developers, desktop apps, and tools; knowledge often expected for full-stack/mobile roles.
Future-proof / career growth rating: 3/5 — indispensable for embedded/local but not a primary server DB for large systems.
⭐ Summary Table
| Database | Best For | Difficulty | Jobs (India) | Future-Proof |
|---|---|---|---|---|
| PostgreSQL | General apps, analytics | Medium | Very High | ⭐⭐⭐⭐⭐ |
| MySQL | Web apps, CMS | Easy | Very High | ⭐⭐⭐⭐☆ |
| MongoDB | Flexible data, startups | Easy | High | ⭐⭐⭐⭐☆ |
| Redis | Performance, caching, AI | Easy–Medium | High | ⭐⭐⭐⭐⭐ |
| SQL Server | Enterprise IT | Medium | High | ⭐⭐⭐⭐☆ |
| Oracle DB | Banking/large systems | Hard | Medium | ⭐⭐⭐☆☆ |
| Elasticsearch | Search & logs | Medium | High | ⭐⭐⭐⭐☆ |
| Cassandra | Large-scale data | Hard | Medium | ⭐⭐⭐⭐☆ |
| DynamoDB | Serverless apps | Medium | High | ⭐⭐⭐⭐☆ |
| SQLite | Mobile/local storage | Very Easy | Medium | ⭐⭐⭐☆☆ |
How to pick based on your career path
- Backend / Full-stack developer: Start with PostgreSQL or MySQL (learn advanced SQL, indexing, joins, migrations). Add Redis (caching) and Elasticsearch (search) later.
- Data engineer / Data platform: Learn PostgreSQL (for analytics), Cassandra or DynamoDB for scale, Elasticsearch for logs, and Redis for fast stateful caching. Also learn streaming (Kafka) and cloud data services.
- DevOps / SRE: Know PostgreSQL / MySQL (admin), Redis (operations), Elasticsearch/OpenSearch (observability), and cluster ops (Cassandra/DynamoDB concepts).
- Mobile / Desktop developer: SQLite + basic server DB (Postgres/MySQL) is sufficient.
- Enterprise / Finance: Oracle and SQL Server skills are still in demand here.
Short learning roadmap
- SQL fundamentals (SELECT, JOIN, indexing) — pick Postgres or MySQL.
- ACID & transactions, performance tuning — dive deeper into Postgres features (JSONB, partitioning).
- Caching & real-time — Redis.
- Search & analytics — Elasticsearch/OpenSearch.
- NoSQL & scale — MongoDB for document models; Cassandra/DynamoDB for massive write scale.
- Cloud managed services (RDS/Aurora, Atlas, DynamoDB, Elastic Cloud) & deployment/backup/monitoring.
Database Trends for 2025
2025 is shaping up to be one of the biggest years for database innovation.
Here are the shifts that matter for your career.
Trend 1: SQL + NoSQL Hybrids Are Becoming Normal
Companies no longer choose one — they use both.
This is why every developer needs hybrid knowledge.
Trend 2: PostgreSQL’s Dominance Will Continue
Its performance upgrades, extensions, and cloud-native adoption are pushing it ahead of old-school systems.
Trend 3: MongoDB Will Expand With AI & Vector Search
Document databases are evolving fast, and MongoDB’s vector search features make it relevant in the AI era.
Trend 4: Redis Becoming a Default Part of Modern Backends
Every fast system uses caching.
Redis is now a must-know skill, not a “nice-to-know.”
Trend 5: Graph Databases Rising Because of AI
Neo4j and other graph databases will grow as recommendation engines and knowledge graphs become mainstream.
Trend 6: Serverless Databases Are Taking Over Mobile Dev
Firebase, Supabase, and PlanetScale are winning because they remove DevOps headaches.
Trend 7: Big Data Workloads Moving to Distributed DBs
Cassandra and distributed SQL (CockroachDB, Yugabyte) are becoming essential for global-scale systems.
Final Recommendation
If you want your database skills to actually help your career in 2025, don’t chase every new tool.
Pick strategically. Learn deeply. Build confidently.
Here’s the simplest path to win:
- Master one SQL database → PostgreSQL or MySQL
- Learn one NoSQL database → MongoDB
- Add Redis for performance
- Explore Neo4j or Cassandra if your career demands it
This combination makes you valuable to startups, enterprises, SaaS companies, AI teams — literally everyone.
Databases aren’t just backend tools anymore.
They are career multipliers.
Start with one. Build projects.
Add more only when you’re ready.
Your future self will thank you.
Conclusion
The database world is evolving faster than ever, and 2025 is the year developers need to understand not just how databases work — but when to use them.
SQL gives you structure, accuracy, and reliability.
NoSQL gives you speed, flexibility, and scale.
Modern apps rely on both.
Whether you’re choosing between PostgreSQL vs MySQL, MongoDB vs SQL, Redis vs MongoDB, or Cassandra vs MongoDB, the real skill is knowing which tool fits which job. And once you understand that, you stop guessing — and start designing systems like a real engineer.
So take the time to learn the foundations.
Build small projects.
Experiment with both SQL and NoSQL.
2025 rewards developers who understand their tools — not just follow trends.
Master your database stack.
And open the doors your career has been waiting for. 🚀
FAQ
1. Which database is best to learn in 2025?
PostgreSQL is widely considered the best database to learn in 2025 due to its performance, ACID compliance, and rapidly growing adoption in startups, SaaS products, and enterprise systems. It’s followed closely by MySQL for web development and MongoDB for NoSQL and modern cloud-native applications.
2. What is the No.1 database in the world?
According to multiple industry rankings, PostgreSQL and MySQL consistently rank at the top because of their stability, wide usage, and open-source ecosystem. MongoDB tops the NoSQL category, while Redis leads in in-memory databases.
3. Which database has the highest demand in the job market?
PostgreSQL, MySQL, MongoDB, and Redis have the highest job demand in 2025. Cloud-managed versions like Amazon RDS, Google Cloud SQL, and MongoDB Atlas are also increasingly requested by employers.
4. Should I learn SQL or NoSQL in 2025?
You should learn both. SQL helps you handle structured, relational data, while NoSQL helps you work with semi-structured, flexible, and high-velocity data. Most companies now run SQL and NoSQL databases together, so knowing both increases your career opportunities.
5. Is PostgreSQL better than MySQL?
PostgreSQL is better for complex queries, analytics, financial systems, and data integrity.
MySQL is better for high-traffic web apps, CMS platforms, and simple transactional workloads.
The “better” database depends on the use-case.
6. Is MongoDB replacing SQL databases?
No. MongoDB isn’t replacing SQL databases, but it complements them.
Modern systems use both: SQL for structured data and NoSQL for flexible, fast-changing data.
7. Which database is best for data science and analytics?
PostgreSQL, Snowflake, BigQuery, and Amazon Redshift are top choices for analytics.
For real-time pipelines, Cassandra and MongoDB can also be useful.
8. Which database is best for mobile and web applications?
- Web apps: MySQL, PostgreSQL
- Mobile apps: Firebase Firestore, MongoDB Atlas, Realm
- Real-time apps (chat, notifications): Redis
9. What is the easiest database to learn for beginners?
MySQL and SQLite are typically the easiest for beginners because of simple syntax, widespread resources, and broad community support.
10. Which database is best for AI and machine learning applications?
For AI systems that rely on relationships (recommendation engines, fraud detection), Neo4j is the top choice.
For flexible document data and rapid iteration, MongoDB is widely used.
11. Which database is best for large-scale applications?
Cassandra, MongoDB, PostgreSQL, and Amazon DynamoDB are commonly used in large-scale distributed systems. They offer high availability, auto-scaling, and massive write/read throughput.
12. Is SQL still worth learning in 2025?
Yes — SQL remains one of the most in-demand skills globally. Almost every company uses SQL databases for critical operations, analytics, and reporting. SQL jobs are increasing, not decreasing.
13. What is the fastest database in the world?
Redis is widely considered the fastest due to its in-memory architecture. It is commonly used for caching, real-time leaderboards, session storage, and high-performance applications.
14. Which database do most companies use?
MySQL and PostgreSQL dominate due to their reliability and cost-efficiency. MongoDB is the most-used NoSQL database. Big companies also use cloud databases like AWS RDS, DynamoDB, BigQuery, and Snowflake.
15. Which database should I learn first?
Start with SQL (MySQL or PostgreSQL) to build foundational understanding.
Then expand to a NoSQL database like MongoDB or Redis depending on your career goal (backend, full-stack, cloud, or mobile development).
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