Database Essentials For Backend Engineers: A full overview

Name: Database Essentials For Backend Engineers: A full overview
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Database Basics and Scalability: Covering relational design, NoSQL strategies, caching solutions, and time series data

Created byJava Brains

Last updated 1/2025

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What you'll learn

Choose between SQL, NoSQL, NewSQL, and specialized databases by understanding their distinct use cases and architectural trade-offs
Master relational databases through schema design, ACID compliance, and SQL querying for traditional enterprise applications
Understand key-value stores for high-speed caching and session management, with strategies for handling data relationships
Learn how applications with document databases like MongoDB, leverage flexible schemas and complex document queries
Learn about columnar databases like Cassandra for distributed data processing and analytics across multiple database instances
Scale applications horizontally using database sharding, replication, and distributed transaction management
Harness time series databases for IoT sensors, market data, and application monitoring with efficient data aggregation
Design systems using NewSQL databases that combine ACID compliance with horizontal scaling for global applications
Integrate Java applications with databases using JDBC and ORM frameworks while optimizing for performance
Understand graph databases for complex relationship mapping in social networks and recommendation systems

Course content

7 sections • 35 lectures • 3h 29m total length

Introduction and goal5:32
Explore the full spectrum of data stores for Java back-end engineers, from relational and document databases to key-value, graph, and time-series options, with guidance on pros, cons, and use cases.
The importance of data storage6:51
Explore how data storage underpins applications, covering transactional data, analytics, content management, session data, caching, and time series and real-time data use cases.
Databases in system design interviews9:37
Learn how to pick databases for system design interviews by analyzing how data is stored, how it is retrieved, and how storage scales, with emphasis on performance and nonfunctional requirements.
Other data storage types2:43
Discover caching strategies to minimize database calls, explore file storage with blob storage and CDNs, and understand data warehouses for offline analytics.
History of databases16:25
Trace the evolution of database management systems from IMS to relational to NoSQL and NewSQL, highlighting data-model shifts, impedance mismatch, and scaling solutions.
Databases in backend development5:57
This lecture explains how databases underpin Java backend development, distinguishing state from logic, introducing JDBC, ORM with Hibernate and JPA, and why relational, document, and key-value stores matter.
Audience questions3:52
Clarify data versus functionality with an e-commerce example, explain when to use a database over files or blob storage for structured data, and summarize JDBC, JPA, Hibernate trade-offs.

Understanding database types12:38
Understand how data storage, querying, and scaling shape database choices. Compare relational databases, NoSQL forms (key value, document, columnar, graph), and newSQL options, with cap theorem considerations.
Audience questions3:20
Explore why NoSQL cannot fully replace traditional RDBMS, highlighting ACID guarantees, consistency, availability, and scalability trade-offs, legacy architectures, and the cost of performance and complexity.
Database models8:16
Compare five database models: relational, columnar, key-value, document, and graph, and see how data storage and operations impact performance, storage efficiency, and distributed node design, with Cassandra and Redshift examples.
Schemas, indexes and transactions7:51
Explore schemas, indexes, and transactions across relational and NoSQL systems, detailing data organization, constraints, and the atomicity, consistency, isolation, and durability properties for integrity.

Relational database - key ideas6:09
Discover core ideas of relational databases, including tables, rows, columns, primary and foreign keys, and how they link data to ensure integrity; grasp schemas and ER diagrams.
ACID compliance5:51
Choose relational databases for acid guarantees, including atomicity, consistency, isolation, and durability, in transactions. Assess scalability tradeoffs and reserve relational options for finance and system design tasks requiring strict integrity.
Audience questions1:12
Understand how acid properties require isolation via locking and serialization, affecting performance. See why relational databases' table- and row-level locks, foreign keys, and distributed system constraints complicate horizontal scalability.
ORM in Java6:56
Learn object-relational mapping in Java using JPA and Hibernate to map entities with annotations, query with JPQL and Criteria API, and understand performance trade-offs.
Pros, cons and when to use relational databases4:15
Relational databases provide ACID guarantees and a standardized SQL interface, with broad maturity and security. Use them when data must be consistent and queries complex, with a fixed schema.
Relational database use case study4:46
Explore a banking system case study to design a relational database with customer, account, transaction, branch, employee, and loan tables, focusing on keys, foreign keys, relationships, and transfers.
Scaling strategies with RDBMS8:42
Scale relational databases with sharding and read replicas to distribute load. Evaluate distributed transactions and new SQL or NoSQL options for asset guarantees in global banking.

Introduction to NoSQL databases4:20
Explore NoSQL databases, learning why they complement modern applications with horizontal scalability, schema-less design, and faster iteration, and compare key value stores, document stores, wide column stores, and graph databases.
Types of NoSQL databases3:45
Explore the types of NoSQL databases, including key-value, document, wide-column, columnar, and graph stores. See examples like DynamoDB, readers, MongoDB, Cassandra, HBase, Neo4j, and Neptune.
Key-value vs document databases3:33
Compare key-value stores and document databases, highlighting retrieval by key, document-level querying, indexing, flexible schemas, and support for complex data structures like arrays and nested documents.
Audience questions1:52
Discover how document stores enable content-based queries—field and date range filters—while noting database-specific differences, such as MongoDB, and learn why schema validation enforces business rules yet allows schema-less querying.
Advantages and disadvantages of NoSQL databases12:28
Assess NoSQL advantages and drawbacks across key-value, document, wide-column, and graph databases, with use cases like session storage, content management, IoT analytics, and social networks.
Strategies for ACID in NoSQL DBs6:47
Explore acid-ish guarantees in NoSQL by balancing atomicity, consistency, isolation, and durability. See how MongoDB achieves atomicity and multi-document transactions, and how Cassandra uses tunable consistency and replica-based durability.
Audience questions3:04
Understand why key value stores lack built-in relationships and how graph databases deliver better performance, a natural network mental model, and graph querying for knowledge graphs and social networking.
Transactions in document databases2:51
Explore how transactions work in document databases like MongoDB, including partial updates with $set, multi-document transactions, and atomic document updates, plus trade-offs with isolation and eventual consistency.

What are NewSQL databases_10:07
Explore how NewSQL databases blend ACID guarantees with partial horizontal scaling, bridging relational and NoSQL paradigms through partitioning and sharding, replication, indexes, concurrency control, and robust crash recovery.
Use case for NewSQL6:41
Explore the use case for NoSQL in high-scale, globally distributed financial applications, covering distributed transactions, two-phase commits, replication, sharding, and CAP theorem considerations.
Audience questions2:02
Explore how sharding splits data by customer ID ranges to localize queries, and how replication enables scalable reads through active replicas while noting write costs.

Time series databases9:55
Time series databases store data with timestamps as a distinct data type from relational databases. They optimize storage and retrieval for time-based queries, support ingest rates, and offer continuous aggregates.
How time series databases work8:57
Time series databases store each new data point as an append-only entry in time-ordered buckets with fixed and dynamic parts, using time as the primary axis and a timestamp.
When to choose time series database4:13
Identify when to choose a time series database based on data characteristics and required query patterns. Use time-based aggregation, downsampling, and recent trends for analytics and visualization.
Disadvantages of time series databases5:00
Examine the disadvantages of time series databases, including data modeling complexity, limited querying capabilities, scalability challenges with high write loads, and a steep learning curve due to unique query languages.
Some use cases for time series databases1:54
Explore practical use cases for time series data, including financial market data and IoT sensor streams. Leverage application monitoring with time series databases like InfluxDB for trend analysis.

Requirements

Basic knowledge of at least one programming language
Familiarity with common system design concepts (client-server interactions, APIs)

Description

Are you preparing for an interview or working on a codebase and need a rock-solid foundation in database technologies? Look no further. Database Essentials for Backend Engineers is a comprehensive dive into the world of data storage, designed to equip you with the knowledge and confidence to make the right decisions about how and where to store your application data.

We begin by exploring why data is considered the lifeblood of modern applications, showing you real-world examples of how poor database choices can cripple even the most innovative ideas. You’ll learn the differences between relational and NoSQL databases—when to prioritize strong consistency, and when horizontal scalability becomes paramount. We’ll cover everything from core SQL and transactional data management to powerful NoSQL paradigms like document stores, key-value pairs, columnar data, and graph databases.

But it doesn’t stop there. This course dives into the practicalities of scaling databases, revealing strategies like read replicas, sharding, and distributed transactions, so your system can handle massive volumes of traffic without skipping a beat. You’ll see how caching mechanisms (using tools like Redis or Memcached) offload work from primary databases, while file storage (such as Amazon S3) and content delivery networks (CDNs) optimize the handling of large static assets.

Moreover, we take a close look at specialized time series databases, unveiling how they streamline real-time analytics for IoT devices, financial markets, and high-velocity logging. By comparing traditional relational systems to emerging NewSQL solutions, you’ll get a front-row seat to the future of global-scale data consistency—ideal for mission-critical services such as payment processing or financial transactions.

Throughout the course, you’ll gain hands-on exposure to Java-based integrations. We’ll discuss JDBC (Java Database Connectivity) for direct queries and explore how Object-Relational Mapping (ORM) frameworks—such as Hibernate and JPA—bridge the gap between object-oriented code and relational data. This full-stack perspective ties together the conceptual and the practical, ensuring you understand not just what to do, but also how to do it.

By the end of Database Essentials for Backend Engineers, you’ll have a powerful toolkit that combines deep conceptual insight with real-world techniques. You’ll be ready to tackle system design interviews, craft robust backend architectures, and confidently select the right database solution to deliver high performance, reliability, and scalability—no matter the size or complexity of your application.

Who this course is for:

Aspiring Backend Engineers: Those looking to solidify their database knowledge and confidently select the right data solution in system design.
Experienced Developers: Professionals needing a refresher on modern database trends (NoSQL, caching, time series) to stay current with industry practices.
System Architects and Tech Leads: Teams or individuals designing high-traffic applications who must balance performance, scalability, and consistency.
Data Enthusiasts: Anyone keen to understand how different storage paradigms support business-critical applications, from banking systems to real-time analytics.

Database Essentials For Backend Engineers: A full overview

What you'll learn

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Course content

Introduction to Databases7 lectures • 51min

Fundamentals of Databases4 lectures • 32min

Relational Databases7 lectures • 38min

NoSQL Databases8 lectures • 39min

NewSQL Databases3 lectures • 19min

Time Series Databases5 lectures • 30min

Conclusion1 lecture • 1min

Requirements

Description

Who this course is for: