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Loading Data from Azure Cloud Storage to AWS Athena with dlt in Python

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Loading data from Azure Cloud Storage to AWS Athena is streamlined using the open-source python library dlt. This verified source can effortlessly stream CSV, Parquet, and JSONL files from Azure Cloud Storage using the reader source. AWS Athena is an interactive query service that simplifies the analysis of data stored in Amazon S3 using standard SQL. Additionally, our implementation supports iceberg tables for enhanced data management. For more details about Azure Cloud Storage, visit this link.

dlt Key Features​

  • AWS Athena / Glue Catalog: The Athena destination stores data as parquet files in S3 buckets and creates external tables in AWS Athena. Learn more
  • Governance Support: dlt pipelines offer robust governance support through pipeline metadata utilization, schema enforcement and curation, and schema change alerts. Learn more
  • Data Loading: Data loading happens by storing parquet files in an S3 bucket and defining a schema on Athena. Learn more
  • Filesystem & Buckets: Filesystem destination stores data in remote file systems and bucket storages like S3, Google Storage, or Azure Blob Storage. Learn more
  • Schema Lineage: dlt provides detailed schema lineage to track changes in your data schema over time. Learn more

Getting started with your pipeline locally​

0. Prerequisites​

dlt requires Python 3.8 or higher. Additionally, you need to have the pip package manager installed, and we recommend using a virtual environment to manage your dependencies. You can learn more about preparing your computer for dlt in our installation reference.

1. Install dlt​

First you need to install the dlt library with the correct extras for AWS Athena:

pip install "dlt[athena]"

The dlt cli has a useful command to get you started with any combination of source and destination. For this example, we want to load data from Azure Cloud Storage to AWS Athena. You can run the following commands to create a starting point for loading data from Azure Cloud Storage to AWS Athena:

# create a new directory
mkdir filesystem_az_pipeline
cd filesystem_az_pipeline
# initialize a new pipeline with your source and destination
dlt init filesystem athena
# install the required dependencies
pip install -r requirements.txt

The last command will install the required dependencies for your pipeline. The dependencies are listed in the requirements.txt:


dlt[athena]>=0.4.3a0
openpyxl>=3.0.0

You now have the following folder structure in your project:

filesystem_az_pipeline/
├── .dlt/
│   ├── config.toml          # configs for your pipeline
│   └── secrets.toml         # secrets for your pipeline
├── filesystem/                   # folder with source specific files
│   └── ...
├── filesystem_pipeline.py        # your main pipeline script
├── requirements.txt         # dependencies for your pipeline
└── .gitignore               # ignore files for git (not required)

2. Configuring your source and destination credentials​

The dlt cli will have created a .dlt directory in your project folder. This directory contains a config.toml file and a secrets.toml file that you can use to configure your pipeline. The automatically created version of these files look like this:

generated config.toml​

# put your configuration values here

[runtime]
log_level="WARNING"  # the system log level of dlt
# use the dlthub_telemetry setting to enable/disable anonymous usage data reporting, see https://dlthub.com/docs/telemetry
dlthub_telemetry = true

[sources.filesystem]
bucket_url = "bucket_url" # please set me up!

generated secrets.toml​

# put your secret values and credentials here. do not share this file and do not push it to github

[sources.filesystem.credentials]
aws_access_key_id = "aws_access_key_id" # please set me up!
aws_secret_access_key = "aws_secret_access_key" # please set me up!

[destination.athena]
dataset_name = "dataset_name" # please set me up!
query_result_bucket = "query_result_bucket" # please set me up!
athena_work_group = "athena_work_group" # please set me up!

[destination.athena.credentials]
aws_access_key_id = "aws_access_key_id" # please set me up!
aws_secret_access_key = "aws_secret_access_key" # please set me up!

2.1. Adjust the generated code to your usecase​

Further help setting up your source and destinations
  • Read more about setting up the Azure Cloud Storage source in our docs.
  • Read more about setting up the AWS Athena destination in our docs.

The default filesystem source is configured to load from AWS S3. To load to Azure Cloud Storage, update the [sources.filesystem.credentials] section in your secrets.toml.

[sources.filesystem.credentials]
azure_storage_account_name="Please set me up!"
azure_storage_account_key="Please set me up!"

You can also set up your bucket_url and file_glob in the config.toml

[sources.filesystem] # use [sources.readers.credentials] for the "readers" source
bucket_url='az://my_bucket'
file_glob="*"

3. Running your pipeline for the first time​

The dlt cli has also created a main pipeline script for you at filesystem_pipeline.py, as well as a folder filesystem that contains additional python files for your source. These files are your local copies which you can modify to fit your needs. In some cases you may find that you only need to do small changes to your pipelines or add some configurations, in other cases these files can serve as a working starting point for your code, but will need to be adjusted to do what you need them to do.

The main pipeline script will look something like this:


import os
import posixpath
from typing import Iterator

import dlt
from dlt.sources import TDataItems

try:
    from .filesystem import FileItemDict, filesystem, readers, read_csv  # type: ignore
except ImportError:
    from filesystem import (
        FileItemDict,
        filesystem,
        readers,
        read_csv,
    )


TESTS_BUCKET_URL = posixpath.abspath("../tests/filesystem/samples/")


def stream_and_merge_csv() -> None:
    """Demonstrates how to scan folder with csv files, load them in chunk and merge on date column with the previous load"""
    pipeline = dlt.pipeline(
        pipeline_name="standard_filesystem_csv",
        destination='athena',
        dataset_name="met_data",
    )
    # met_data contains 3 columns, where "date" column contain a date on which we want to merge
    # load all csvs in A801
    met_files = readers(
        bucket_url=TESTS_BUCKET_URL, file_glob="met_csv/A801/*.csv"
    ).read_csv()
    # tell dlt to merge on date
    met_files.apply_hints(write_disposition="merge", merge_key="date")
    # NOTE: we load to met_csv table
    load_info = pipeline.run(met_files.with_name("met_csv"))
    print(load_info)
    print(pipeline.last_trace.last_normalize_info)

    # now let's simulate loading on next day. not only current data appears but also updated record for the previous day are present
    # all the records for previous day will be replaced with new records
    met_files = readers(
        bucket_url=TESTS_BUCKET_URL, file_glob="met_csv/A801/*.csv"
    ).read_csv()
    met_files.apply_hints(write_disposition="merge", merge_key="date")
    load_info = pipeline.run(met_files.with_name("met_csv"))

    # you can also do dlt pipeline standard_filesystem_csv show to confirm that all A801 were replaced with A803 records for overlapping day
    print(load_info)
    print(pipeline.last_trace.last_normalize_info)


def read_csv_with_duckdb() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="standard_filesystem",
        destination='athena',
        dataset_name="met_data_duckdb",
    )

    # load all the CSV data, excluding headers
    met_files = readers(
        bucket_url=TESTS_BUCKET_URL, file_glob="met_csv/A801/*.csv"
    ).read_csv_duckdb(chunk_size=1000, header=True)

    load_info = pipeline.run(met_files)

    print(load_info)
    print(pipeline.last_trace.last_normalize_info)


def read_csv_duckdb_compressed() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="standard_filesystem",
        destination='athena',
        dataset_name="taxi_data",
        full_refresh=True,
    )

    met_files = readers(
        bucket_url=TESTS_BUCKET_URL,
        file_glob="gzip/*",
    ).read_csv_duckdb()

    load_info = pipeline.run(met_files)
    print(load_info)
    print(pipeline.last_trace.last_normalize_info)


def read_parquet_and_jsonl_chunked() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="standard_filesystem",
        destination='athena',
        dataset_name="teams_data",
    )
    # When using the readers resource, you can specify a filter to select only the files you
    # want to load including a glob pattern. If you use a recursive glob pattern, the filenames
    # will include the path to the file inside the bucket_url.

    # JSONL reading (in large chunks!)
    jsonl_reader = readers(TESTS_BUCKET_URL, file_glob="**/*.jsonl").read_jsonl(
        chunksize=10000
    )
    # PARQUET reading
    parquet_reader = readers(TESTS_BUCKET_URL, file_glob="**/*.parquet").read_parquet()
    # load both folders together to specified tables
    load_info = pipeline.run(
        [
            jsonl_reader.with_name("jsonl_team_data"),
            parquet_reader.with_name("parquet_team_data"),
        ]
    )
    print(load_info)
    print(pipeline.last_trace.last_normalize_info)


def read_custom_file_type_excel() -> None:
    """Here we create an extract pipeline using filesystem resource and read_csv transformer"""

    # instantiate filesystem directly to get list of files (FileItems) and then use read_excel transformer to get
    # content of excel via pandas

    @dlt.transformer(standalone=True)
    def read_excel(
        items: Iterator[FileItemDict], sheet_name: str
    ) -> Iterator[TDataItems]:
        import pandas as pd

        for file_obj in items:
            with file_obj.open() as file:
                yield pd.read_excel(file, sheet_name).to_dict(orient="records")

    freshman_xls = filesystem(
        bucket_url=TESTS_BUCKET_URL, file_glob="../custom/freshman_kgs.xlsx"
    ) | read_excel("freshman_table")

    load_info = dlt.run(
        freshman_xls.with_name("freshman"),
        destination='athena',
        dataset_name="freshman_data",
    )
    print(load_info)


def copy_files_resource(local_folder: str) -> None:
    """Demonstrates how to copy files locally by adding a step to filesystem resource and the to load the download listing to db"""
    pipeline = dlt.pipeline(
        pipeline_name="standard_filesystem_copy",
        destination='athena',
        dataset_name="standard_filesystem_data",
    )

    # a step that copies files into test storage
    def _copy(item: FileItemDict) -> FileItemDict:
        # instantiate fsspec and copy file
        dest_file = os.path.join(local_folder, item["relative_path"])
        # create dest folder
        os.makedirs(os.path.dirname(dest_file), exist_ok=True)
        # download file
        item.fsspec.download(item["file_url"], dest_file)
        # return file item unchanged
        return item

    # use recursive glob pattern and add file copy step
    downloader = filesystem(TESTS_BUCKET_URL, file_glob="**").add_map(_copy)

    # NOTE: you do not need to load any data to execute extract, below we obtain
    # a list of files in a bucket and also copy them locally
    # listing = list(downloader)
    # print(listing)

    # download to table "listing"
    # downloader = filesystem(TESTS_BUCKET_URL, file_glob="**").add_map(_copy)
    load_info = pipeline.run(
        downloader.with_name("listing"), write_disposition="replace"
    )
    # pretty print the information on data that was loaded
    print(load_info)
    print(pipeline.last_trace.last_normalize_info)


def read_files_incrementally_mtime() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="standard_filesystem_incremental",
        destination='athena',
        dataset_name="file_tracker",
    )

    # here we modify filesystem resource so it will track only new csv files
    # such resource may be then combined with transformer doing further processing
    new_files = filesystem(bucket_url=TESTS_BUCKET_URL, file_glob="csv/*")
    # add incremental on modification time
    new_files.apply_hints(incremental=dlt.sources.incremental("modification_date"))
    load_info = pipeline.run((new_files | read_csv()).with_name("csv_files"))
    print(load_info)
    print(pipeline.last_trace.last_normalize_info)

    # load again - no new files!
    new_files = filesystem(bucket_url=TESTS_BUCKET_URL, file_glob="csv/*")
    # add incremental on modification time
    new_files.apply_hints(incremental=dlt.sources.incremental("modification_date"))
    load_info = pipeline.run((new_files | read_csv()).with_name("csv_files"))
    print(load_info)
    print(pipeline.last_trace.last_normalize_info)


if __name__ == "__main__":
    copy_files_resource("_storage")
    stream_and_merge_csv()
    read_parquet_and_jsonl_chunked()
    read_custom_file_type_excel()
    read_files_incrementally_mtime()
    read_csv_with_duckdb()
    read_csv_duckdb_compressed()

Provided you have set up your credentials, you can run your pipeline like a regular python script with the following command:

python filesystem_pipeline.py

4. Inspecting your load result​

You can now inspect the state of your pipeline with the dlt cli:

dlt pipeline filesystem_pipeline info

You can also use streamlit to inspect the contents of your AWS Athena destination for this:

# install streamlit
pip install streamlit
# run the streamlit app for your pipeline with the dlt cli:
dlt pipeline filesystem_pipeline show

5. Next steps to get your pipeline running in production​

One of the beauties of dlt is, that we are just a plain Python library, so you can run your pipeline in any environment that supports Python >= 3.8. We have a couple of helpers and guides in our docs to get you there:

The Deploy section will show you how to deploy your pipeline to​

  • Deploy with GitHub Actions: Utilize GitHub Actions for free CI/CD to automate your dlt pipeline deployments.
  • Deploy with Airflow: Leverage Google Composer to manage your dlt pipelines using Airflow.
  • Deploy with Google Cloud Functions: Implement Google Cloud Functions to deploy serverless dlt pipelines.
  • Explore Other Deployment Methods: Check out additional deployment options for deploying dlt pipelines in various environments.

The running in production section will teach you about:​

  • How to Monitor your pipeline: Learn how to effectively monitor your dlt pipeline in production to ensure smooth operation and quick issue resolution. Read more
  • Set up alerts: Configure alerts to get notified about important events and potential issues in your dlt pipeline. Read more
  • Set up tracing: Implement tracing in your dlt pipeline to gain detailed insights into its execution and performance. Read more

Additional pipeline guides​

This demo works on codespaces. Codespaces is a development environment available for free to anyone with a Github account. You'll be asked to fork the demo repository and from there the README guides you with further steps.
The demo uses the Continue VSCode extension.

Off to codespaces!

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