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14 Top Data Pipeline Key Terms Explained

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 Here are some key terms commonly used in data pipelines 1. Data Sources Definition: Points where data originates (e.g., databases, APIs, files, IoT devices). Examples: Relational databases (PostgreSQL, MySQL), APIs, cloud storage (S3), streaming data (Kafka), and on-premise systems. 2. Data Ingestion Definition: The process of importing or collecting raw data from various sources into a system for processing or storage. Methods: Batch ingestion, real-time/streaming ingestion. 3. Data Transformation Definition: Modifying, cleaning, or enriching data to make it usable for analysis or storage. Examples: Data cleaning (removing duplicates, fixing missing values). Data enrichment (joining with other data sources). ETL (Extract, Transform, Load). ELT (Extract, Load, Transform). 4. Data Storage Definition: Locations where data is stored after ingestion and transformation. Types: Data Lakes: Store raw, unstructured, or semi-structured data (e.g., S3, Azure Data Lake). Data Warehous...
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IoT application project need for BTech ECE students

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Iot projects
IOT Project
IoT provides networking to connect people, things, applications, and data through the Internet to enable remote control, management, and interactive integrated services. IoT network scale, how large is it? Well, you have to think of this. The number of mobile devices exceed the number of people on Earth.

In addition, predictions are made that there will be 50 billion 'things' connected to the Internet by 2020. So therefore, Internet of Things, this study is so important. 

IoT Service Support. Some advanced IoT services will need to collect, analyze, and process segments of raw sensor information, raw sensor data, and we need to turn this into operational control information. Some sensor data types may have massive sizes, because the number of sensor IoT devices are so large. 

circuits
#Introduction to Elecronic circuit analysis

Also Read: Project and Training on IoT

So we need a platform that can collect and store all of this massive amount of information. IoT databases will be needed, and that's where Cloud Computing Support will be needed. IoT data analysis will be needed, and that's where big data support will be needed. The influence of IoT can be seen in people, processes, data, and things.

First, people. More things can be monitored and controlled, so people will become more capable. Process-wise, more users and machines can collaborate in real time, so more complex tasks can be accomplished in lesser amount of time because now we have more collaborative, more coordinated efforts that can be pulled together.

Data wise. Collect data more frequently and reliably. Well that would result in more accurate decision making. Things. Things become more controllable. So therefore, mobile devices and things become more valuable. There's more that you can do with them. What is the overall economic impact? Predictions have been made that IoT has the potential to increase global corporate profits by 21% by 2022.

Where is this all coming from? It's a combination of asset utilization, employee productivity, supply chain and logistics improvement, customer experience, and other type of combined innovations.

Also read: Take free course on IOT

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