Startup Data Stacks
Startup Data Stacks
Jul 1, 2023

Demystifying Databases: A Practical Guide for Startups

Demystifying Databases: A Practical Guide for Startups

Welcome back to our Startup Data Stacks series, where we aim to make the complex world of tech and data infrastructure a little simpler for early-stage startups. This series is born out of real-life lessons, sleepless nights, and the drive to help others avoid the same pitfalls.

Today's post delves into databases, a critical component of your data stack. From SQL vs NoSQL, OLAP vs OLTP, to the blazing-fast realm of In-memory databases and the strategic choices of Managed vs Self-managed databases, we've got it all covered. Plus, we're also sharing some popular database options and vendors offering free trials. So, prepare yourself. We're about to unpack the vital knowledge that could streamline your startup's data-driven strategies.

Now that we know what's ahead, let's delve into our first topic: the critical choice between SQL and NoSQL databases. Understanding this decision can greatly influence your startup's data operations. Let's get started.

SQL vs NoSQL: Making Sense of the Spectrum

Diving into the evergreen debate of SQL vs NoSQL - let's discern the best fit for your startup's unique needs.

SQL Databases

SQL databases, also known as relational databases, structure data into predefined tables and rows. SQL databases utilize a schema, which is a predefined structure of how data is organized. 

Popular Databases

MySQL, Oracle, PostgreSQL, and MS SQL Server.

When to Use SQL Databases

    - When data integrity is essential.

    - When you need to perform complex queries.

    - When your data is structured and unchanging.

Pros of SQL Databases

Structured Data: SQL databases are excellent at handling structured data. They organize data into tables, which makes data management highly efficient.

ACID Compliance: SQL databases are ACID compliant, ensuring the reliability of transactions. This is especially important for systems like banking, where data integrity is crucial.

Standardized Language: SQL is a standardized language that is widely recognized and used. This makes it easy to manage and query data in SQL databases.

Powerful and Flexible: SQL databases can handle complex queries and operations. They also provide powerful features like views, stored procedures, and triggers.

Mature and Well-Supported: SQL databases have been around for decades. There's a large community of developers, extensive documentation, and many resources for support.

Cons of SQL Databases

Scalability: SQL databases typically scale vertically by adding more power (CPU, RAM) to an existing machine. Horizontal scaling (across multiple machines) is more challenging with SQL databases compared to NoSQL databases.

Flexibility: SQL databases require a predefined schema, which can be less flexible for certain types of applications. If data is varied and changes often, making alterations to the schema can be difficult and time-consuming.

Complexity: While the power of SQL databases is a pro, it can also be a con. Writing complex SQL queries can be difficult and requires specific knowledge and experience. Watch the demo video to know how Airdot solves this pain point using AI.

Cost: Many popular SQL databases are proprietary and can be expensive to use, especially for larger systems. However, there are also open-source options.

Use Cases for SQL Databases

Online Shopping Systems: SQL databases are perfect for managing inventories and transactions in online shopping systems. The relational nature of SQL databases is well-suited to manage the relationships between different entities such as customers, orders, products, and payments, and transactions ensure that inventory and order data remain consistent.

Banking Systems: Banking applications require high consistency, data integrity, and complex transactions. SQL databases offer all these capabilities, making them ideal for managing customer accounts, transactions, loans, etc.

Healthcare Systems: In healthcare systems, SQL databases can manage and store patient records, doctors' schedules, patients' appointments, medical billing information, etc. Here, data accuracy and consistency are paramount, and SQL databases are well-equipped to handle these requirements.

NoSQL Databases

NoSQL databases are non-relational databases that can store and process a wide range of data types and structures, including key-value, document, columnar, and graph formats. 

Popular Databases

MongoDB, DynamoDB, Cassandra, and CouchDB.

When to Use NoSQL Databases

    - When you need horizontal scalability.

    - When you’re working with large amounts of data with diverse structures.

    - When you need the flexibility to store diverse data types (text, images, video links, etc).

Pros of NoSQL Databases

Scalability: One of the major advantages of NoSQL databases is their ability to scale horizontally. This means you can easily add more servers to handle more traffic and data.

Flexibility: Unlike SQL databases that require predefined schemas, NoSQL databases are schema-less. This allows for flexibility in storing different types of data and makes it easier to adjust to changing requirements.

Speed: NoSQL databases are often faster than SQL databases for certain types of queries, especially when dealing with large amounts of unstructured data.

Variety of Data Models: NoSQL databases support a variety of data models including key-value, document, columnar and graph formats, which can be better suited for different types of applications.

Handling Big Data: NoSQL databases are designed to handle big data and real-time applications, providing efficient, scale-out architecture for large data loads.

Cons of NoSQL Databases

Less Mature: NoSQL databases are newer than their SQL counterparts. While many have strong communities and robust features, they don't have the same level of maturity or the extensive resources available with SQL databases.

Lack of Standardization: There's no standard query language for NoSQL databases, and they often require proprietary languages to query. This can create a learning curve and a lack of interoperability between different NoSQL systems.

Complexity: While they offer flexibility, the variety of data models can add complexity. It also requires different thinking about how data is stored and queried, which can lead to a steep learning curve.

Transactional Support: Many NoSQL databases do not support ACID transactions, or only offer eventual consistency. This makes them less suitable for applications where data consistency is critical.

Use Cases for NoSQL Databases

Real-time Big Data Analytics: NoSQL databases like Cassandra or HBase are often used in real-time big data analytics platforms because of their ability to handle large volumes of data and provide fast responses. They can store and process unstructured or semi-structured data, which is often the case with big data.

Content Management Systems and Catalogs: NoSQL databases like MongoDB are great for content management systems and product catalogs, where the data structure may vary and evolve over time. They can handle diverse data types and structures and offer the flexibility to accommodate changing data requirements.

Internet of Things (IoT) Applications: NoSQL databases like Amazon DynamoDB or Couchbase are often used in IoT applications because of their ability to handle large amounts of data and provide fast responses. They can efficiently store and retrieve time-series data generated by IoT devices.

Having cleared the air about SQL and NoSQL, welcome to the next pit-stop: OLAP Vs OLTP.

OLTP vs OLAP: Transactions vs Analytics

Why is there even a distinction between OLAP and OLTP in the first place, and what do transactions versus analytics mean in this context? As you delve into the world of databases, you'll notice different systems are optimized for different tasks. OLTP systems, often write-heavy, excel in managing and processing transactions swiftly. On the other hand, OLAP systems are typically read-heavy, designed to sift through, analyze, and report on data efficiently. When trying to use a single system for both tasks, you may find yourself in a tug of war between speed and analysis. Thus, we distinguish between OLTP and OLAP, each tailored to excel in its unique workload.

Online Transaction Processing (OLTP) Databases

OLTP databases are application-oriented databases that manage day-to-day transactional data. They are designed to handle a large number of short online transactions. These databases prioritize fast, reliable data manipulation and are characterized by a large number of short online transactions (INSERT, UPDATE, DELETE).

When to use

OLTP databases are the optimal choice when you need to manage and update high volumes of transactional data quickly, reliably, and accurately.

Popular Databases

MySQL, Oracle Database, Microsoft SQL Server, PostgreSQL

Online Analytical Processing (OLAP) Databases

OLAP databases, on the other hand, are information-oriented databases used for data analysis. They use complex queries to process aggregated, historical data, pulled from OLTP databases and other sources.

When to use

OLAP databases are the go-to choice when you need to perform complex analytical queries and derive insights from large volumes of historical and aggregated data.

Popular Databases

Amazon Redshift, Google BigQuery, Snowflake, Apache Druid

The Big Question - Are NoSQL Databases OLAP or OLTP?

NoSQL databases can serve both OLTP and OLAP needs:

OLTP: Types like document databases (MongoDB), Key-Value stores (DynamoDB), and wide-column stores (Cassandra) excel in high transaction rates, often where data is written as much as it is read.

OLTP: Types like document databases (MongoDB), Key-Value stores (DynamoDB), and wide-column stores (Cassandra) excel in high transaction rates, often where data is written as much as it is read.

While these general guidelines apply, some NoSQL databases like Apache Cassandra can be tailored for both OLTP and OLAP use cases due to their distributed architecture and high availability.

To help grasp the nuances between SQL vs NoSQL and OLAP vs OLTP, here is the comparison table:

SQL Databases NoSQL Databases
Type Relational Non-Relational
OLAP/OLTP Both (Many SQL databases can handle OLAP and OLTP) Primarily OLTP (Some NoSQL databases have limited OLAP capabilities)
Query Language SQL Database-Specific
Structure Table-based with a fixed schema Varies (document, key-value, wide-column, graph), flexible or schema-less
Scalability Vertical scaling Horizontal scaling
ACID Compliance Yes Typically No
Popular Databases MySQL, PostgreSQL, Oracle, MS SQL Server, BigQuery, Redshift, Snowflake MongoDB, Cassandra, CouchDB, DynamoDB, HBase
Use Cases Structured and consistent data, complex transactions, analytical and transactional processing Large volumes of varying data types, rapid scaling needs, real-time applications
Consistency Strong consistency Eventual or tunable consistency

With SQL vs NoSQL and OLAP vs OLTP unraveled, let's shift gears to another fundamental element in the data stack: In-memory databases, where speed is the name of the game.

In-memory Databases: Racing with RAM

In-memory databases primarily rely on main memory (RAM) for data storage, unlike traditional databases that store data on disk. But why do we even need them? When latency matters in scenarios like real-time analytics or high-frequency trading, in-memory databases are a game-changer. They offer blazing-fast read and write speeds because RAM is exponentially faster than disk storage. 

Popular Databases

Redis, Memcached, Aerospike, Apache Ignite, DynamoDB DAX

When to Use 

    - For ultra-fast data access and processing.

    - To perform real-time analytics.

    - When data volatility isn't a concern.

    - For quick processing of data-intensive applications.

    - If the cost for memory resources isn't a limiting factor.

Pros of In-memory Databases

Speed: In-memory databases are incredibly fast due to data storage in memory, which helps reduce latency.

Scalability: Many in-memory databases support distributed architectures, allowing them to scale horizontally as demand increases.

Real-time Processing: These databases are perfect for real-time data processing needs.

Cons of In-memory Databases

Cost: Memory is more expensive than disk storage, which might be a limiting factor for some applications.

Data Volatility: Since data is stored in memory, in case of a power outage or system crash, there's a risk of data loss unless the system has persistence or replication features.

Use Cases for In-memory Databases

Real-Time Analytics: Analyze massive datasets for instantaneous insights. Perfect for: Redis, Aerospike.

Gaming Applications: Real-time data access for improved player experience like stats, leaderboards. Perfect for: Redis, DynamoDB DAX.

Ad-Tech Platforms: Swiftly process high volumes of real-time user data to serve relevant ads. Perfect for: Aerospike.

High-Frequency Trading: Make split-second trading decisions with vast market data processing. Perfect for: TimesTen.

Real-Time Recommendation Systems: Offer instant recommendations based on user behavior and preferences. Perfect for: Redis, Aerospike.

Caching: Provide faster data access to frequently used data, improving application performance and reducing database load. Perfect for: Redis, Memcached.

While in-memory databases might be the speed demons of the data world, not all databases can or should be maintained in-house. This brings us to the next crucial decision point - should you manage your database internally or opt for a managed service?

Managed vs Self-Managed Databases: Weighing the Pros and Cons

Certainly, every database requires maintenance, updates, security, and more, but should you bear this burden yourself, or let the experts handle it? This dilemma is at the heart of the Managed vs Self-Managed databases debate.

Managed Databases

Managed databases, also known as Database as a Service (DBaaS), are a service model where the maintenance, scaling, backup, and patching of databases are handled by the service provider. Users are freed from the operational aspects of running a database, allowing them to focus on their core business.

Examples: Amazon RDS, Google Cloud SQL, Azure SQL Database.

When to use

    - You lack database expertise.

    - Resource and time are constrained.

    - Rapid scaling is required.

Pros of Managed Databases

Ease of Use: The service provider handles maintenance, backups, and scaling, reducing the complexity of database management.

Scalability: Most providers offer easy scalability to accommodate business growth.

Focus on Development: With the database management taken care of, your team can focus more on application development.

Cons of Managed Databases

Cost: Managed services can be expensive, especially for larger databases.

Limited Customization: Some specific configurations or optimizations might not be possible.

Dependency: You are dependent on the service provider for uptime and potential issues.

Use Cases for Managed Databases

Startups and Small Businesses: For resource-limited companies needing expert support to ensure stability, performance, and security.

E-commerce Platforms: High-traffic platforms requiring flexible scalability, automated backups, and high availability.

SaaS Applications: Companies focused on improving their application and customer service while outsourcing database operations.

Self-Managed Databases

With self-managed databases, the organization or individual is responsible for the entirety of the database operations, including installing, configuring, maintaining, securing, backing up, and scaling the database. This offers greater control and customization over the database but requires dedicated time, resources, and expertise.

Examples: Any database system like MySQL, PostgreSQL, or MongoDB installed and managed on-premises or on a cloud instance (like AWS EC2) can be classified as self-managed.

When to use

    - You require specific customizations.

    - Cost-effectiveness is a priority.

    - Full control over the database is necessary.

Pros of Self-Managed Databases

Full Control: You have complete control over the database, including configuration, optimization, and security measures.

Cost-Effective: Over time, self-managing can be more cost-effective, especially for large databases.

No Vendor Lock-in: You are free to move your database to different servers or platforms as needed.

Cons of Self-Managed Databases

Requires Expertise: Effective management of databases requires a certain level of knowledge and experience.

Time-Consuming: Database management can be time-consuming, taking resources away from other tasks.

Maintenance Responsibility: You're responsible for all updates, backups, and disaster recovery plans.

Use cases for Self-Managed Databases

Large Enterprises: Companies with in-house IT teams desiring full control over database optimization, security, and configuration.

Research Institutions: Organizations needing custom database setups for complex queries and unique data types.

Regulated Industries: Industries such as healthcare or finance that require strict data control and security.

For startups, choosing managed databases with smaller instances is a wise move. This choice frees up resources and allows teams to focus more on product development than on database management.

We've journeyed through various database concepts in the previous sections. To tie it all together, the following summary comparison table showcases the capacities of some popular databases, helping you in your selection process.

DB Type Suited For On-Prem Cloud Setup
BigQuery OLAP Large data analytics No Yes Easy
MySQL OLTP Web apps, data warehousing Yes Yes Mod
PostgreSQL OLTP Web apps, geo-apps Yes Yes Mod
MongoDB OLTP (NoSQL) Real-time analytics, content mgmt. Yes Yes Mod
Snowflake OLAP Large data analytics No Yes Easy
Redshift OLAP Large data analytics No Yes Mod
ClickHouse OLAP Real-time analytics Yes Yes Mod-Hard
DynamoDB OLTP (NoSQL) High-velocity, large scale apps No Yes Easy
Athena OLAP Ad-hoc data analysis No Yes Easy
SQL Server OLTP Enterprise apps, BI analytics Yes Yes Mod
Redis In-memory DB Caching, real-time analytics, queuing Yes Yes Mod
Memcached In-memory DB High-speed caching, reducing DB load Yes Yes Mod
DAX In-memory DB Speeding up DynamoDB response times No Yes Easy
Aerospike In-memory DB Real-time transactional apps Yes Yes Mod-Hard

As we've navigated the technicalities of different database types, it's time to ground this knowledge with some practical advice. Let's shift gears and focus on practical tips that can assist you in your startup journey.

Practical Tips

In the dynamic world of startups, certain databases have emerged as go-to solutions for particular needs. It's important to remember that you can - and most startups do - use multiple databases for powering various use cases or services. It's not an "either-or" scenario, rather, a clever mix-and-match based on specific needs.

SQL databases, for example, are essential for most startups to manage production-critical data like userIDs, contentIDs, SKU, and more. Postgres stands out in this realm, as it capably serves both OLTP and OLAP tasks.

Meanwhile, for startups handling data like audio, video, and images—data that doesn't require the joining of other data—NoSQL databases make a good fit. DynamoDB and MongoDB are top choices in this category, often used for specific use cases such as storing audio links or managing user-generated content.

In scenarios where caching and real-time operations like recommendations are vital, in-memory databases like Redis and Memcached are often the popular choices. These databases can power high-speed services, such as search or real-time analytics, significantly improving the performance of these services. 

To help startups explore these options, numerous database providers offer free versions of their services:

Redis Labs: A free tier of managed Redis database for caching and real-time use cases.

MongoDB Atlas: A free tier of managed NoSQL database for storing unstructured data.

Google Cloud Firebase: A free tier for a NoSQL database, good for web, mobile, and server development.

Amazon RDS: A free tier of relational database service for managing structured, relational data.

Microsoft Azure Cosmos DB: A free tier of multi-model NoSQL database for globally distributed applications.

IBM Db2 on Cloud: A free Lite plan for a hybrid data approach.

Amazon DynamoDB: A free tier offering 25GB of storage.

Amazon Redshift: A free trial of their data warehousing solution.

Amazon MemoryDB: A free tier offering a managed in-memory database compatible with Redis.

Snowflake: A 30-day free trial with $400 worth of credits to use across all Snowflake’s cloud-built services.

That's a wrap for this blog post, folks! Our aim was to help guide you through the database terrain, offering insights earned through our own trial and error. We hope we've managed to shed some light and bring a little clarity to your startup journey. We are, as always, just an email away at for any comments, questions, or further assistance. Keep your eyes peeled for the next installment in our series where we'll explore 'Defining and Tracking Key Performance Indicators for Startups.' Until then, keep building and growing!