IoT-database-architecture

To choose the best database architecture for an organization’s IoT initiative, IoT technologists must understand the basics of all the database options.

IoT-database-architecture
IoT database architecture

IoT database architecture- Organizations have many options to choose from when designing an IoT database, but technologists must decide the best fit by evaluating the different IoT database architectures, such as static vs. streaming and SQL vs. NoSQL.

The right IoT database depends on the requirements of each IoT project. The first step to select a database is to factor in critical characteristics of IoT when selecting among database architectures. IoT technologists must determine the types of data to be stored and managed; the data flow; the functional requirements for analytics, management and security; and the performance and business requirements.

After identifying the organization’s requirements for a database, IT admins must assess the IoT database architectures and how they will promote or inhibit IoT data needs.

Understand static and streaming IoT database architectures

Start by understanding the fundamental distinction between static and streaming databases. Static databases, also known as batch databases, manage data at rest. Data that users need to access resides as stored data managed by a database management system (DBMS). Users make queries and receive responses from the DBMS, which typically, but not always, uses SQL. A streaming database handles data in motion. Data constantly streams through the database, with a continuous series of posed queries, typically in a language specific to the streaming database. The streaming database’s output may ultimately be stored elsewhere, such as in the cloud, and accessed via standard query mechanisms.

Streaming databases are typically distributed to handle the scale and load requirements of vast volumes of data. Currently, there are a range of commercial, proprietary and open source streaming databases, including Google Cloud Dataflow, Microsoft StreamInsight, Azure Stream Analytics, IBM InfoSphere Streams and Amazon Kinesis. Open source systems are largely based around Apache and include Apache Spark Streaming provided by Databricks, Apache Flink provided by Data Artisans, Apache Kafka provided by Confluent and Apache Storm, which is owned by Twitter. Organizations mainly use streaming databases for real-time decision-making and to meet near-instantaneous latency requirements.

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Article Credit: TechTarget

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