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In Chapter 3 (Working with NoSQL alternatives) we want to turn tables on the typical
discussion around NoSQL.We choose to not include Cosmos DB in the chapter, either to
postpone the topic to a dedicated book, either to highlight how many NoSQL alternatives
we have in Azure outside the classics. We center the discussion around Blobs, that are
often under-evaluated, around Tables and Redis to finally approach on Azure Search,
one of the most promising managed search services in the cloud ecosystem.
In Chapter 4 (Orchestrate data with Azure Data Factory) we discover orchestration
of data. We want to emphasize the importance of data activities, in terms of movements,
transformation and the modern addressing to the concepts we known as ETL for many
years. With Data Factory, you will discover an emerging (and growing up) service to deal
with pipelines of data and even complex orchestration scenarios.
In Chapter 5 (Working with Azure Data Lake Store and Azure Data Lake Analytics)
we start to build foundations for the big data needs. We discover how Data Lake can help
with storing, managing and analyzing unstructured data, stored in their native format
while they are generated. We will learn this important lesson around big data: since we
are generating and storing today the data we are using and analyzing tomorrow, we need
a platform service to build intelligence on it with minimal effort.
Finally, Chapter 6 (Working with In-Transit Data and Analytics) closes the book
with a little introduction about messaging and, generally, the in-transit data, to learn
how we can take advantage of ingestion to build run-time logics in addition to the most
consolidated ones. Messaging is extremely important for several scenarios: almost every
distributed system may use messaging to decouple components and micro-services.
Once messaging is understood, we can apply the event-based reasoning to move some
parts of the business rules before the data is written to the final, persistent data store.
Eventually, we learn how to implement in-transit analytics.
We hope this can be a good cue to address how to approach data service in this
promising momentum of cloud and Platform-as-a-Service. We know this book cannot
be complete and exhaustive, but we tried to focus on some good points to discuss the
various areas of data management we can encounter on a daily basis.
inTRoduCTion