What are some alternatives to traditional relational databases for data storage?

- NoSQL databases (e.g., MongoDB, Cassandra) - Key-Value stores (e.g., Redis) - Column-family stores (e.g., HBase) - Document stores (e.g., Couchbase) - Graph databases (e.g., Neo4j) - NewSQL databases (e.g., Google Spanner) - In-memory databases (e.g., Memcached) - Time-series databases (e.g., InfluxDB) - Object storage (e.g., Amazon S3) - Distributed file systems (e.g., Hadoop HDFS)

NoSQL stands for "not only SQL" meaning it's more than SQL and can be referred to a wide range of database technologies that do not use the traditional relational data model. Various NoSQL databases to consider can be: - Document Databases (e.g., MongoDB, Couchbase): - Key-Value Stores (e.g., Redis, DynamoDB) - Column-Family Stores (e.g., Apache Cassandra, HBase) - Graph Databases (e.g., Neo4j, Amazon Neptune) - Time-Series Databases (e.g., InfluxDB, OpenTSDB) - NewSQL Databases (e.g., Google Spanner, CockroachDB) - In-Memory Databases (e.g., Redis, Memcached) - Object Databases(For applications with complex data structures) NoSQL databases are a viable alternative to traditional relational databases.

Modeling and deciding on the access pattern is of paramount importance when selecting a NoSQL database structure. This critical step ensures that the database is tailored to meet specific application requirements, optimizing data retrieval and performance. By carefully designing the access patterns, developers can harness the full potential of NoSQL databases to efficiently store and retrieve data, ultimately enhancing scalability and responsiveness in modern, data-intensive applications.

NoSQL databases can be used to store diverse types of data and do not need to be as structured as SQL databases. They allow for great adaptability and flexibility, making it a more suitable choice when handling large sets of unstructured and unrelated data. There are four major kinds of NoSQL databases: document databases, key-value databases, wide-column stores, and graph databases. Document databases store data in documents similar to JSON objects. Each document contains pairs of fields and values. Key-value pair databases as the name indicates have each item that contains keys and values. Wide-column stores data in tables, rows, and dynamic columns. Graph databases store data in nodes and edges.

NoSQL databases are a compelling alternative to traditional relational databases. These innovative systems offer a flexible and scalable approach to data management, making them particularly well-suited for handling large volumes of unstructured or semi-structured data. Unlike rigid tabular structures, NoSQL databases allow for easy adaptation to changing data models and schemas, making them an excellent choice for today's dynamic and data-intensive applications. As businesses increasingly seek real-time analytics and need to support web-scale applications, NoSQL databases offer robust solutions that can deliver high performance and availability.

Imagine a modern, improved version of traditional SQL databases. They aim to combine the benefits of traditional SQL (structured data) with the scalability of NoSQL. Suitable for applications where both structure and scalability are essential.

NewSQL databases are basically modern day relational database systems which bridge the gap between SQL and NoSQL systems. NewSQL database basically combines ACID compliance with horizontal scaling feature. ClustrixDB, NuoDB, CockroachDB, Pivotal GemFire XD, Altibase, MemSQL, VoltDB, c-treeACE, Percona TokuDB, Apache Trafodion, TIBCO ActiveSpaces, ActorDB are some of the Top NewSQL Databases.

??We could also think of Open Table formats like Hudi, Iceberg; which solves the challenges with traditional databases and currently being used for managing data in data lakes(typically s3/adls/hdfs). Table Formats provides capabilities that enable multiple applications to work together on the same data in a transaction consistent manner and functionality as SQL tables in traditional databases but in a fully open and accessible manner such that multiple engines(spark/presto) which enables it to scale.

Data warehouse in today's trend of processing data as ELT/ETL is one of the preferred alternative for traditional relational databases. Few advantages to consider can be Analytics, Performance, Scalability and Data Integration whereas, High Maintenance, latency, cost and data silos could be considered as drawbacks for considering data warehouse as an alternative to traditional relational databases for data storage.

As Data grows in volume and with the growing need for analytics/ historical reporting. Organisations would need a Data Warehouse to store their data. While the major cloud vendors are highly recommended for this. With proper modeling of data, Smaller Orgs who can't afford these can spin up regular databases like Postgres on-prem or in the cloud temporarily and move operational data from source systems into it for analytical processes. The beauty of data warehouses is that compute is seperated from storage .

Think of it as an organized, structured storage system for business data. Data is cleaned, transformed, and organized for easy querying and reporting. Used for making informed business decisions with reliable data.

One of the most important aspects of selecting a data warehouse solution is scalability. As an organization's data grows over time, the chosen data warehouse must be able to handle increased data volumes, processing power, and concurrent users easily. Scalability ensures that the data warehouse can adapt to evolving business needs without compromising performance or incurring excessive costs. Additionally, the data warehouse solution should support various data sources, provide robust security features, and offer flexible querying and analytics capabilities to empower users in extracting meaningful insights from the stored data.

Data warehouses(DW) are the go-to solution for the following specialized use cases when considering them for data storage: 1. Analytics: Unlike operational databases(relational) focusing on CRUD operations, DW is optimized for complex analytical queries. 2. Aggregation and Computation: DW allows for extensive aggregation, joining, and computation across large datasets without affecting operational systems. 3. High Concurrency: DWs are built to accommodate many simultaneous users and queries, making them ideal for enterprise-level reporting and analytics. 4. Historical Data: Unlike operational systems that purge old data, warehouses can maintain historical data, enabling trend analysis over extended periods.

On one hand data lakes offer flexibility, reliability, scalability and cost efficiency, on the other side it can cause complexity, data governance and can compromise with data quality as well. Whereas, considering data lake is an alternative approach and it completely depends on the business use case to store large volumes of raw, unstructured, or semi-structured data.

If you are designing or building Delta Lake, evaluate and consider Lake House architecture that uses Delta Lake. Delta Lake is an open-source storage layer that brings ACID transactions to data lakes. It can be a valuable addition to data lakes when you require data consistency, reliability, and structured processing capabilities.

Picture a big lake where you dump all your data from various sources. Data is stored in its raw, unprocessed form, and you can analyze it later. Useful when you have lots of diverse data and want flexibility in analyzing it.

Data lakes offers highly optimized storage data from various sources and process the data on demand. In few scenarios, Data lake can be hard to manage especially when it contains unstructured data and can cause privacy issue if its not properly protected. Data lake consumes a lot of bandwidth and network resources if it needs to transfer large amount of unstructured data and lacks metadata, which leads to expensive operations to store and process with in cloud services.

Imagine your computer's memory (RAM) is used to store data instead of a hard drive. It's super fast because accessing data in memory is quicker than reading from a disk. Used for applications that require lightning-fast data access, like some websites or apps.

There is a popular belief that putting data onto RAM will be tremendously faster. It is faster, but still need to consider how data will be navigated: disk, NVMe or RAM - one still needs to traverse a tree by going though nodes. There might be cases where expensive in-memory setup may not really provide that dramatic difference.

In my eyes, it's the most common approach in the big data world. There are a lot of engines built to read directly and run SQL queries on top of S3/GCS and Azure Blob Storage. Benefits: store whatever you want, high availability (at least AWS gives 11 9s), no locks - amazing for concurrency. Downside: Storing whatever will be hard to maintain, you have to implement a locking mechanism or use i.e. Iceberg/Delta format to have some sort of ACID'ity available. When choosing a tool - always think about the use case. There is no one shoe fits all.

Consider factor while choosing databases : SQL Databases (Structured Data): Ideal for structured data with fixed schemas OLTP : Designed for high transaction volumes and real-time processing. Eg: MySQL, PostgreSQL, Oracle. OLAP: Optimised for complex queries and large dataset reporting. Eg: redshift,bigquery. Amazon Redshift. NoSQL Databases (Semi-Structured): Flexible schema design. Excellent for high concurrency and scalability. Types include Document-Oriented, Key-Value, Graph, Column-Oriented, Time Series, and Wide-Column Stores. NewSQL Databases: Blend SQL and NoSQL benefits for scalability and ACID compliance. Suitable for both scalability and strong consistency. Examples: Google Spanner, CockroachDB.

Think of it like a digital storage unit for your files and data. It stores data as objects (like files) rather than in a structured way. Good for storing large amounts of unstructured data, like videos, images, or backups.

Object storages are a great way to integrate non-structured and structured data in a single place, also known as data lake. In this scenario, the concept of ELT is introduced as a way to ingest data into an environment where it can be transformed when and how it is most useful for business needs. It has a much more flexible way to deal with data and a large array of tools and methods that can be applied, varying from managed cloud services (in cloud providers such as AWS, Azure and GCP) to open source tools. These tools can be used to help manage the downsides mentioned previously and help you get the best experience handling data.

Another important concept which is missing in the above article is "Lake Warehouse". The hybrid approach of "Data Lake" and "Data Warehouse", getting benefits of both worlds. Hudi, Iceberg and Delta Lake are few technologies supporting Lake Warehouse concept which can be used on top of AWS S3, Azure Blob storage or any other object storage.

This article focuses on alternatives to relational databases, and then offers data warehouses as an alternative. This is incorrect, as a data warehouse uses relational schemas for its data organisation, basically being organized the same as a relational database. A data lake is also not a correct alternative, as a data lake combines relational and non-relational data together. If you are looking for other data storage options that are not relational, there are various other options such as graph databases, streaming databases, and virtualized databases.

Multi-model databases like Cosmos DB offer the ability to handle different data models within a single database system. They provide flexibility to work with various data structures and relationships, depending on the specific requirements of the application.

A Data Lakehouse is a type of data storage architecture that combines the best features of both data lakes and data warehouses. It is a central repository for all types of data, both structured and unstructured, and it provides the ability to query and analyze that data quickly and efficiently. Data lakes are typically used to store large amounts of raw data, such as sensor data, log files, and customer images. Data warehouses, on the other hand, are used to store structured data that has been cleaned and transformed for analytics purposes. Data lakehouses combine the flexibility and scalability of data lakes with the performance and manageability of data warehouses.

One should first understand the usecase / problem. All data storage options have their benefits and weaknesses. I would not use a datalake / datawarehouse for operational datastorage. You should stick to a relational database in this case. For data analytics and data science, you should then move to other storages systems. Also for fast response times in web applications, we should better use some nosql or in-memory databases. Datalakes will not provide the data fast enough for this use case. So there are alternatives to relational database, but there are not necessary better for everything