NEAR is a decentralized blockchain platform that aims to provide a scalable and developer-friendly environment for building decentralized applications (dApps). It was launched in 2018 and is built using a unique consensus algorithm called "Proof of Stake-Thresholded Proof of Stake" (PoS-TPoS), which is designed to offer a high level of security and efficiency.

NEAR aims to address some of the key challenges facing existing blockchain platforms, such as scalability, usability, and developer adoption. It uses sharding to partition the network into smaller sub-networks, which allows for higher transaction throughput and faster processing times. Additionally, NEAR's developer tools and APIs are designed to be intuitive and easy to use, making it accessible to a wide range of developers.

NEAR also features a built-in token, called NEAR, which is used to pay for transaction fees and other network services. The NEAR Foundation, a non-profit organization, oversees the development and governance of the platform and supports the NEAR ecosystem through grants, investments, and partnerships.

Overall, NEAR aims to provide a user-friendly, scalable, and secure platform for building decentralized applications and fostering the growth of the decentralized web.

In this dashboard, my focus is to review the flows of the NEAR Centralized Exchange (CEX). I will assess various CEXs based on metrics such as the number of users, transactions, and transfer volumes. Additionally, I will analyze the activities of users who have engaged with CEXs across the entire ecosystem.

I use the following metrics to analyze NEAR Centralized Exchange (CEX) Flows:

The Metrics based on Inflows/Outflows :

1. Weekly number of transactions based on CEX flows over time.
2. Weekly number of users transferred assets from/to CEXes over time.
3. Weekly transferred NEAR volume from/to CEXes over time.
4. Total Number of transacions from/to CEXes.
5. Total number of users transferred from/to CEXes.
6. Total Transferred volume from/to CEXes.
7. Average Transferred volume from/to CEXes per user.
8. Average Transferred volume from/to CEXes per transaction.
9. Weekly average transferred volume over time.
10. Distribution of number of users based on outflow volume.
11. Distribution of number of transactions based on outflow volume.
12. Distribution of number of users based on inflow volume.
13. Distribution of number of transactions based on inflow volume.

The Metrics based on CEXes :

1. Monthly number of transactions based on CEXes over time.
2. Monthly number of users based on CEXes over time.
3. Monthly transferred volume based on CEXes over time.
4. Top CEXes based on inflow transactions.
5. Top CEXes based on number of users transferred assets to CEXes.
6. Top CEXes based on transferred volume to CEXes.
7. Top CEXes based on Transferred volume from CEXes.
8. Top CEXes based on number of users transferred assets to CEXes.
9. Distribution of number of transactions based on Transferred volume.
10. Distribution of number of users based on Transferred volume range.

The Metrics based on users activity :

1. Monthly numbr of user transactions on the whole ecosystem based on interacted with CEXes.
2. Monthly number of active users based on interacted with CEXes.
3. Total user Transactions on whole ecosystem based on interacted with CEXes.
4. Total number of active users on whole ecosystem based on interacted with CEXes.
5. Top 20 Dapps based on number of users interacted with CEXes.
6. Top 20 Dapps based on number of transactions by users interacted with CEXes.
7. Total users interacted with protocols based on protocol tyoe.

To extract the addresses of centralized exchanges, I utilized the near.core.dim_address_labels table, applying a filter for LABEL_TYPE = 'cex'. Next, I examined transactions related to asset transfers to/from CEXs by utilizing the near.core.fact_transfers table. To gauge user activity, I also analyzed data from the near.core.fact_transactions table.