Performance issue of azure.datalake.store.core.AzureDLFile.write() for Cosmos

### Description

I ran benchmark to compare the performance to upload data to Cosmos between **AzureDLFile.write()** and the write API from **native libhdfs.so**. The result show there is significant gap. To write same amount data to Cosmos, the time used by azure data lake store is **more than double** of the time HDFS used. I also checked the network throughput, with HDFS we can push it to about **4Gb.** And for ADL the throughput is only reach to **1.3Gb.**

In my testing, I used multiple thread to write the data. Each thread creates individual file and write data into it. I tried to increase the thread number and the buffer size. it didn't help to improve the performance.

My questions are:
1. Is this performance gap expected? Since azure data lake store is based on REST API.
2. Is there any advanced API or parameter I can try to improve the throughput? For my scenario, we have to use the streaming write API to upload the data.

### Environment summary

**SDK Version:** What version of the SDK are you using? (pip show azure-datalake-store)
Answer here: The latest.

**Python Version:** What Python version are you using? Is it 64-bit or 32-bit?
Answer here: python version: 3.6.9 64

**OS Version:** What OS and version are you using?
Answer here: Ubuntu 18.04

Inizia da azure.datalake.store.core.AzureDLFile.write() e dal confronto riportato con libhdfs.so nativa. Riproduci il benchmark di scrittura in streaming su Python 3.6.9 e Ubuntu 18.04, quindi determina se il divario di throughput è previsto oppure se una API o un parametro documentato lo migliora.

Scritto dal modello di indicizzazione a partire dal testo della issue.