ngods

KO | EN

gitlite — search

#javascript #ai #typescript #machine-learning #deep-learning #nodejs #windows #awesome #kubernetes #pytorch #security #mcp #openai #open-source

(★ 74)

New generation opensource data stack

ngods 최신버젼 다운로드

최종 버전 다운로드 (.zip)

// repository documentation

Was this content helpful?

★ 0 (0 ratings)

# ngods: opensource data stack
This repository contains Docker compose script that creates opensource data analytics stack on your local machine.

![ngods architecture](https://raw.githubusercontent.com/zsvoboda/ngods/main/img/ngods.png)

Currently, the stack consists of multiple components:

- [**Trino**](https://trino.io/) for low-latency distributed SQL queries 
- [**Apache Spark**](https://spark.apache.org/) for data pipeline
- [**Apache Iceberg**](https://iceberg.apache.org/) for atomic data pipeline with schema evolution, and partitioning for performance 
- [**Hive Metastore**](https://cwiki.apache.org/confluence/display/hive/design#Design-Metastore) for metadata management (metadata are stored in MariaDB)
- [**Minio**](https://min.io/) for mimicking S3 storage on a local computer

I plan to add more components soon:

- [**Postgres**](https://www.postgresql.org/) for low-latency queries (if Trino on Iceberg doesn't provide satisfactory low-latency queries). I'll also add Postgre Foreign Data Wrapper technology for more convenient ELT between Trino and Postgres
- [**DBT**](https://www.getdbt.com/) for ELT on top of Spark SQL or Trino
- [**GoodData.CN**](https://www.gooddata.com/developers/cloud-native/) or [**Cube.dev**](https://cube.dev/) for analytics model and metrics 
- [**Metabase**](https://www.metabase.com/) or [**Apache Superset**](https://superset.apache.org/) for dashboards and data visualization

## How ngods works: Simple example
You can simply start the ngds by executing

```bash
docker-compose up
```

from the top-level directory where you've cloned this repo.

Once all images are pulled and all containers start, open Minio in your browser [http://localhost:9000](http://localhost:9000) log in with ```minio``` username and  ```minio123``` password and create a top level bucket called ```bronze``` .

Then use your favorite SQL console tool (I use [DBeaver](https://dbeaver.io/)) and connect to the Trino instance running on your local machine (jdbc url: ```jdbc:trino://localhost:8080```, username: ```trino```, empty database) and execute the following script:

```sql
create schema if not exists warehouse.bronze with (location = 's3a://bronze/');

drop table if exists warehouse.bronze.employee;

create table warehouse.bronze.employee (
   employee_id integer not null,
   employee_name varchar not null
)
with (
   format = 'parquet'
);

insert into warehouse.bronze.employee (employee_id, employee_name) values (1, 'john doe');
insert into warehouse.bronze.employee (employee_id, employee_name) values (2, 'jane doe');
insert into warehouse.bronze.employee (employee_id, employee_name) values (3, 'joe doe');
insert into warehouse.bronze.employee (employee_id, employee_name) values (4, 'james doe');

select * from warehouse.bronze.employee;
```

## How ngods works: Loading parquet file as table
Now we'll load the February 2022 NYC taxi trip data parquet file as a new table to ngods.

First, create a new ```nyc``` directory in the ```./data/stage``` directory and download the [February 2022 NYC taxi trips parquet file](https://s3.amazonaws.com/nyc-tlc/trip%20data/fhvhv_tripdata_2022-02.parquet) to it.

Then open and execute the ngods [Spark notebook script](http://localhost:8888/notebooks/notebooks/spark.nyc.taxti.example.ipynb) that loads the data as a new table to the ```bronze``` schema of a ```warehouse``` database.

```python
df=spark.read.parquet("/home/data/stage/nyc/fhvhv_tripdata_2022-02.parquet")
df.writeTo("warehouse.bronze.ny_taxis_feb").create()
```

Now open your SQL console again, connect to the the Trino instance running on your local machine (jdbc url: ```jdbc:trino://localhost:8080```, username: ```trino```, empty database) and execute this SQL queries:

```sql
select count(*) from warehouse.bronze.ny_taxis_feb;

select 
	hour(pickup_datetime),
	sum(trip_miles),
	sum(trip_time),
	sum(base_passenger_fare)
from warehouse.bronze.ny_taxis_feb group by 1 order by 1;
```

You should see your query results in no time!

ngods

ngods 최신버젼 다운로드

오늘의 추천 프로젝트

Was this content helpful?

Recent Feedback

ngods

ngods 최신버젼 다운로드

오늘의 추천 프로젝트

Was this content helpful?

Recent Feedback

Download README