The simplest form of storage is called Blob storage. It’s referred to as object storage, but really it’s just a collection of files. It’s not like a normal filesystem, though, because it doesn’t have a hierarchical folder structure. It has a flat structure. It’s typically used for unstructured data, such as images, videos, and log files.

One of the great things about it is that it has multiple access tiers: hot, cool, and archive. The hot tier is for frequently accessed files. The cool tier is for files you expect to access only about once a month or less. The advantage is that it costs less than the hot tier as long as you don’t access it frequently. The archive tier is for files that are rarely accessed, such as backup files. It has the lowest storage costs but the highest retrieval costs. It also takes several hours to retrieve files from the archive tier.

If you need hierarchical file storage, there are a couple of options. The one that will probably seem more familiar is Azure File Storage, which is like a typical SMB file server. It serves up file shares that you can mount on Windows servers. The less familiar option is Azure Data Lake Storage Gen2. This is Hadoop-compatible storage for use with data analytics applications.

Okay, now how about relational databases? In an on-premises Microsoft environment, SQL Server is the most commonly used database. The cloud equivalent is Azure SQL Database. It’s very similar to SQL Server, although it’s not 100% compatible. If you need to run an open-source database, then Microsoft still has you covered. It offers Azure Database for MySQL, MariaDB, and PostgreSQL.

All of these databases, including both SQL Database and the open-source options, are suitable for online transaction processing. On the other hand, if you need to build a data warehouse, then Azure Synapse Analytics is the best choice.

If you release an application that attracts a very large number of users, you may find that a traditional relational database can’t scale to meet the demand. One common solution is to use a so-called NoSQL database. These databases are designed to handle far more data than relational databases. However, in order to achieve that massive scalability, they have to sacrifice something, so they don’t support all of the features of relational databases. Nonetheless, they have become a cornerstone of many cloud-based applications.

Microsoft’s main NoSQL offering is called Cosmos DB. It’s an amazing database service that can scale globally. Another NoSQL service is Azure Cache for Redis, which is typically used to speed up applications by caching frequently requested data.

That’s a lot of storage options, and I still didn’t cover them all, but you don’t need to know every single option at this point.

When you have a large volume of data coming in, you’ll probably want to perform analytics on it. Microsoft’s analytics offerings have evolved over time, which is why you’ll see a variety of services in this area.

The oldest one is HDInsight. It supports a wide variety of open-source big data frameworks, including Hadoop, Spark, Hive, Storm, and many others. Azure Databricks is a similar service because it runs Spark as well, but it’s more user-friendly and easier to manage than HDInsight. Azure Synapse Analytics is the new version of Azure SQL Data Warehouse. It includes all of the old data warehouse functionality, but it also supports Spark analytics. Have you noticed a common theme? Apache Spark seems to be the king of big data analytics, and it’s just a question of which service you want to use to run it.

Okay, that’s it for this storage, database, and analytics services overview.