title: Best approach to extract 1000 Gbs of Data from Teradata
body: <p>What should be the best approach to download data from Teradata using python ? I have more than 3000 GBs of data to be migrated from teradata to another code service. Any approach or advise would be great.</p>
  <p>So far I have explored -</p>
  <ol>
  <li>Using Pandas it is slow and inefficient. I have also used {fn teradata_try_fastexport} but it seems to be no difference.</li>
  <li>Using tdload I am not able to partition the file and no support for downloading file with byte,clob,blob data type.</li>
  <li>Using dask, not able to connect with teradata and get the data using dask dataframe. I have read the sql using pandas and then use dask to write the data. However, that seems to be of little help in comparison to huge amount of data.</li>
  <li>Pyspark - I am exploring but it seems difficult with too much file configuration and dependency issue.</li>
  </ol>
  <p>Any advise or suggested approach that I should follow for the efficient and effective data extraction.</p>