Featured Post

14 Top Data Pipeline Key Terms Explained

-
Image
 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...
Post a Comment

Internet Of Things Awesome Basics You Need to Read Now: Part-4

-

How propagator nodes will work in communicating devices in IOT: As noted previously, replicating even this highly efficient chirp protocol traffic indiscriminately throughout the IoT would clearly choke the network, so intelligence must be applied at levels above the individual end devices.

iot basics for beginnners
  • This is the responsibility of propagator nodes, which are devices that create an overarching network topology to organize the sea of machine-to-machine interactions that make up the Internet of Things.
  • Propagator nodes are typically a combination of hardware and software distantly similar to WiFi access points. They handle "local" end devices, meaning that they interact with end devices essentially within the (usually) wireless transmission range of the propagator node. They can be specialized or used to receive chirps from a wide array of end devices. Eventually, there would be tens or perhaps hundreds of thousands of propagator nodes in a city like Las Vegas. 
  • Propagator nodes will use their knowledge of adjacencies to form a near-range picture of the network. They will locate in-range nearby propagator nodes, as well as end devices and integrator functions either attached directly to or reached via those propagator nodes. This information is used to create the network topology: eliminating loops and creating alternate paths for survivability.
The propagator nodes will intelligently package and prune the various chirp messages before broadcasting them to adjacent nodes. Examining the public markers, the simple checksum, and the "arrow" of transmission (toward end devices or toward integrator functions), damaged or redundant messages will be discarded. Groups of messages that are all to be propagated via an adjacent node may be bundled into one "meta" message-a small data "stream"-for efficient transmission. Arriving "meta" messages may be unpacked and repacked.

Some classes of propagator nodes will contain a software publishing agent. This publishing agent interacts with particular integrator functions to optimize data forwarding on behalf of the integrator. Propagator nodes with publishing agents may be "biased" to forward certain information in particular directions based on routing instructions passed down from the integrator functions interested in communicating with a particular functional, temporal, or geographic "neighborhood" of end devices. 

It is the integrator functions that will dictate the overall communications flow based on their needs to get data or set parameters in a neighborhood of IoT end devices.

Comments

Post a Comment

Thanks for your message. We will get back you.

Popular posts from this blog

How to Fix datetime Import Error in Python Quickly

-
Image
Here's a quick resolution for import datetime Python error . The reason is your .py python script name and datetime  are the same. I'll show you how this error happens and its resolution.   Here's the Resolution for ImportError I've created a script called 'datetime.py.' to check whether the minute value is 'odd' or not. During the import of my python script, I got the import error cannot import name datetime. My Script: datetime.py My script's intention is to find whether the minute value is odd or not. Python Logic from datetime import datetime odds = [ 1 , 3 , 5 , 7 , 9 , 11 , 13 , 15 , 17 , 19 , 21 , 23 , 25 , 27 , 29 , 31 , 33 , 35 , 37 , 39 , 41 , 43 , 45 , 47 , 49 , 51 , 53 , 55 , 57 , 59 ] right_this_minute = datetime.today().minute if right_this_minute in odds: print ( "This minute seems a little odd." ) else : print ( "Not an odd minute." ) ImportError I have imported my datetime.py from Linux. It gives an error ...
Read more

SQL Query: 3 Methods for Calculating Cumulative SUM

-
Image
SQL provides various constructs for calculating cumulative sums, offering flexibility and efficiency in data analysis. In this article, we explore three distinct SQL queries that facilitate the computation of cumulative sums. Each query leverages different SQL constructs to achieve the desired outcome, catering to diverse analytical needs and preferences. Using Window Functions (e.g., PostgreSQL, SQL Server, Oracle) SELECT id, value, SUM(value) OVER (ORDER BY id) AS cumulative_sum  FROM your_table; This query uses the SUM() window function with the OVER clause to calculate the cumulative sum of the value column ordered by the id column. Using Subqueries (e.g., MySQL, SQLite): SELECT t1.id, t1.value, SUM(t2.value) AS cumulative_sum FROM your_table t1 JOIN your_table t2 ON t1.id >= t2.id GROUP BY t1.id, t1.value ORDER BY t1.id; This query uses a self-join to calculate the cumulative sum. It joins the table with itself, matching rows where the id in the first table is greater than...
Read more

Big Data: Top Cloud Computing Interview Questions (1 of 4)

-
Image
The below are frequently asked interview questions on Cloud computing: 1) What is the difference between Cloud and Grid? Grid: -Information service -Security Service -Data management -Execution Manageement Cloud: - Maintains up-to-date information of resources -Create VMs according to user requirement -Application deploment -User management 2) What are the different cloud standards? -Interoperability standards -Security standards -Portability Standards -Governance and Risk standards 3) What are the two different sub-systems in Cloud computing ? -Management sub system -Resource sub system 4)What is Cloud compouting? The promise of cloud computing is ubiquitous access to a broad set of applications and services, which are delivered over the network to multiple customer. 5) Why we need specialized network for Cloud services? The public Internet is the simplest choice for delivering cloud-based services. In this model, the cloud provider simply purchases Inter...
Read more