This document proposes a real-time big data analytical architecture for remote sensing applications to address scalability issues in handling huge amounts of data. The architecture includes a remote sensing data acquisition unit to collect raw data, a data processing unit to filter and load balance the useful data, and a data analytics decision unit to compile results and generate decisions. It also describes algorithms for filtration and load balancing, processing and calculation, aggregation and compilation, and decision making.

1. Real-Time Big Data Analytical Architecture for Remote Sensing
Application:
Point to remember:
1. What is remote sensing?
2. What is big data?
3. What is data analytical architecture?
4. What is data processing?
Problem statement:
1. Scalability issues, which refer to the application, are likely to be running on large scale.
2. Extraction transformation loading method from low, raw data to well thought-out data up to certain extent;
3. Scalabledata management has been a vision for more than three decades and much research has
focused on largescaledata management in traditional enterprisesetting.
4. The run-time system takes careof the details of partitioningthe input data,schedulingthe program’s
execution across a setof machines,handlingmachinefailures,and managingthe required inter-machine
communication.
5. Difficult to handle & maintain huge amount in remote sensing application.
Proposed architectures:
1. Remote sensing big data acquisition unit.(RSDU)
Description of RSDU:
It collects the raw data from the earth atmosphere and send it to the ground station via
downlink channel.
2. Data processing unit (DPU).
Description of RSDU:
The collected raw data information are separated with the help of filtration and load
balancing algorithm , useful data for analysis since it only allows useful information,
whereas the rest of the data are blocked and are discarded.

2. 3. Data analytics decision unit (DADU).
Description of DADU:
DADU, which is responsible for compilation, storage of the results, and generation of
decision based on the results received from DPU.
Proposed algorithms:
1. Filtration and Load Balancing Algorithm.
Description:
This algorithm takes satellite data or product and then filters and divides them into segments and
performs load-balancing algorithm.
2. Processing and Calculation Algorithm.
Description:
The processing algorithm computes the results for dissimilar restrictions against each incoming block
and sends themto the next level.
3. Aggregation and Compilation Algorithm.
Description:
It collects the results from each processing servers against each and then combines, organizes, and
stores these results in RDBMS database.
4. Decision-making algorithm.
Description:
The algorithm varies from requirement to requirement and depends on the analysis needs.