0:03 Welcome. Today we will explore database
0:05 systems designed for the internet of
0:07 things. This presentation will cover
0:09 storage solutions specifically tailored
0:11 for the unique challenges and demands of
0:15 IoT data. We will explore databases, the
0:18 internet of things and data
0:21 storage. The world of IoT presents
0:23 several data related
0:25 challenges. First, the massive volume of
0:28 data is generated continuously.
0:30 Second, heterogeneous data formats and
0:33 structures are involved. Third,
0:36 real-time processing requirements exist.
0:38 Fourth, resource constraints are present
0:40 on edge devices. And finally,
0:42 scalability and distribution present
0:43 their own
0:45 challenges. Consider the diagram showing
0:48 cloud storage, gateway, and various
0:50 devices. The connections between them
0:53 indicate the flow and storage of data,
0:59 There are several types of databases
1:02 available for internet of things. Let's
1:03 start with relational
1:05 databases. These databases use
1:08 structured data with a fixed schema and
1:09 support acid
1:11 transactions. You can perform complex
1:14 queries with SQL. However, they have
1:16 limited scalability for large volumes of
1:19 IoT data. Examples of relational
1:22 databases include Poster SQL, MySQL,
1:26 SQLite, and Timecale DB. Next are NoSQL
1:28 databases which have a flexible schema
1:31 for varied internet of things data. They
1:34 offer horizontal scalability, high write
1:36 throughput and better support for time
1:39 series data. Examples include MongoDB,
1:43 Cassandra, Influx DB and
1:46 Reddus. Beyond the traditional database
1:49 types, several specialized options cater
1:51 specifically to the needs of Internet of
1:53 Things applications.
1:55 Time series databases are optimized for
1:57 handling timestamp data points collected
2:00 at regular intervals from sensors and
2:02 devices. These are suited for sensor
2:04 data monitoring, performance metrics,
2:07 and environmental tracking. Edge
2:09 databases are lightweight databases
2:11 designed to run on edge devices with
2:14 limited resources, enabling local data
2:17 processing and storage. These are useful
2:19 for smart home devices, industrial
2:21 controllers, and offline first applications.
2:22 applications.
2:24 Stream processing systems process
2:27 continuous data streams in real time,
2:29 enabling immediate analysis and response
2:32 to data. These are helpful in real time
2:35 analytics, anomaly detection, and predictive
2:40 maintenance. Let's dive deeper into time
2:42 series databases for the internet of
2:44 things. They offer high-speed data
2:47 ingestion, timebased data organization,
2:49 efficient time range queries, data
2:51 compression and downsampling and
2:54 retention policies with time to live.
2:56 Some popular solutions include Influx
3:00 DB, Timecale DB, Prometheus and Amazon
3:02 time stream. Here is an Influx DB
3:04 example using
3:07 InfluxQL. First create a database for
3:09 Internet of Things sensor data. Then
3:13 insert temperature data points. Finally,
3:16 query the average temperature by
3:19 location. Let's take a closer look at
3:20 edge computing
3:22 databases. They offer a lightweight
3:25 footprint, offline first operation, low
3:28 latency access, energyefficient design,
3:30 and synchronization capabilities with
3:33 the cloud. Popular edge databases
3:37 include SQLite, Rox DB, Level DB, and
3:40 Pouch DB. Consider the diagram that
3:42 depicts an edgetocloud database
3:44 architecture. The diagram illustrates
3:46 how data flows from the edge devices
3:49 through the edge gateway to the cloud
3:51 databases. This setup helps in local
3:54 storage, local processing, and efficient
4:00 Stream processing for IoT data offers
4:02 real-time data analysis, a continuous
4:05 processing model, immediate anomaly
4:08 detection, dynamic data aggregation, and
4:11 pattern recognition in motion. Popular
4:13 stream processing systems include Apache
4:16 Kofka streams, Apache Flink, Apache
4:19 Spark streaming, and Amazon Web Services
4:22 Kinesis. This is a Kafka streams
4:24 example. First define a stream
4:26 processing topology.
4:28 Then create an input stream from
4:30 temperature sensors. Filter readings
4:32 above a certain threshold and send
4:35 alerts for high temperatures. Stream
4:37 processing is useful in industrial
4:39 equipment monitoring, connected vehicle
4:46 infrastructure. IoT systems pose several
4:50 challenges. One is data volume. IoT
4:52 systems can generate terabytes of data
4:54 daily from thousands or millions of
4:56 connected devices, overwhelming
4:59 traditional database systems. The
5:00 solutions here involve distributed
5:03 database architectures, data compression
5:05 techniques, automated data tearing
5:08 strategies, and edge filtering to reduce
5:10 cloud data. Another challenge is
5:13 performance and latency. IoT
5:15 applications often require real-time
5:17 data processing and low latency
5:19 responses, especially for critical
5:21 systems like industrial controls or
5:23 healthcare monitoring. Possible
5:25 solutions are in memory database
5:28 technologies, edge computing databases,
5:31 optimized indexing strategies, and
5:34 caching layers for frequent queries. The
5:37 final challenge is data heterogeneity.
5:39 IoT devices produce diverse data types
5:41 including structured metrics,
5:44 semistructured logs, unstructured text,
5:47 images, and video streams from various
5:49 sources. Solutions include multimodel
5:53 databases, schemalless NoSQL solutions,
5:55 data lakes with unified access, and
6:00 transformation. Here are a few best
6:03 practices for IoT databases.
6:05 One, design for scale from the
6:08 beginning. Two, implement data life
6:11 cycle management. Three, balance edge
6:14 and cloud processing. Four, prioritize
6:17 security and privacy. And five, optimize
6:20 for rightheavy workloads. Looking
6:21 forward, there are a few future trends
6:24 to keep an eye on. These are artificial
6:26 intelligence powered database
6:28 optimization for IoT workloads,
6:31 serverless database platforms for IoT,
6:33 blockchain integration for data
6:35 integrity, federated databases across
6:38 edge networks, and quantum computing for
6:40 complex IoT
6:43 analytics. If you like this video, hit
6:46 that like button and don't forget to
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