> ## Documentation Index > Fetch the complete documentation index at: https://docs.ocient.com/llms.txt > Use this file to discover all available pages before exploring further. # Data Pipeline Load of Parquet Data from S3 export const TimeKey = "TimeKey®"; export const Parquet = "Apache® Parquet™"; export const Ocient = "Ocient®"; export const Metabase = "Metabase℠"; export const AWS = "Amazon® Web Services℠ (AWS℠)"; A common setup for loading files in a batch into the {Ocient} System is to load from a bucket on {AWS} S3 with time-partitioned data. Often, you must perform a batch load repeatedly to load new files. The Ocient System uses data pipelines to transform each document into rows in one or more different tables. The loading and transformation capabilities use a simple SQL-like syntax for transforming data. This tutorial guides you through a simple example load using a small data set in {Parquet} format. The data in this example comes from a test set for the {Metabase} Business Intelligence tool. ## Parquet Loading Recommendations Follow this set of recommendations for an optimal loading experience of Parquet files. **File Configuration** * Files should have row groups of less than 128 MB. Larger row groups can impact memory usage during loading, and row groups of 512 MB can cause loading failures on 1 TB or more data sets. * Encoding fields in a Parquet file reduces the space of the file on disk but can impact memory usage during loading. Enable encoding on fields that you expect to have less than 256 unique values and for fields that contain short strings. You do not have to encode other fields. **Multiple Files** * You can load row groups of multiple Parquet files in parallel. For large data sets, load the data set as multiple files. * Loading files with differing schemas is not supported. ## Prerequisites This tutorial assumes that: 1. The Ocient System has network access to S3 from the Loader Nodes. 2. An Ocient System is installed and configured with an active Storage Cluster. For details, see [Ocient Application Configuration](/ocient-application-configuration). ## Parquet Loading Example Follow these steps to load Parquet data into the Ocient System. ### Step 1: Create a New Database Connect to a SQL Node using the [Commands Supported by the Ocient JDBC CLI Program](/commands-supported-by-the-ocient-jdbc-cli-program). Then, execute the `CREATE DATABASE` SQL statement to create the `metabase` database. ```sql SQL theme={null} CREATE DATABASE metabase; ``` ### Step 2: Create a New Table in the Database Create the `orders` table in the new database. First, connect to that database (e.g., `connect to jdbc:ocient://sql-node:4050/metabase`), and then execute this `CREATE TABLE` SQL statement that specifies to create a table with these columns and a clustering index based on the `user_id` and `product_id` columns: * `created_at` as a {TimeKey} timestamp that is not nullable. * `id`, `user_id`, and `product_id` as integers that are not nullable. * `subtotal`, `tax`, `total`, and `discount` as floating point numbers. * `quantity` as an integer. ```sql SQL theme={null} CREATE TABLE public.orders ( created_at TIMESTAMP TIME KEY BUCKET(30, DAY) NOT NULL, id INT NOT NULL, user_id INT NOT NULL, product_id INT NOT NULL, subtotal DOUBLE, tax DOUBLE, total DOUBLE, discount DOUBLE, quantity INT, CLUSTERING INDEX idx01 (user_id, product_id) ); ``` The database creates the `orders` table, and you can begin loading data. ### Step 3: Preview and Create a Data Pipeline Create data pipelines using the `CREATE PIPELINE` SQL statement. To load data, you first create a pipeline with the definition of the source, data format, and transformation rules using a SQL-like declarative syntax. Then, you execute the `START PIPELINE` SQL statement to start the load. You can observe progress and status using system catalog tables and views. Each Ocient pipeline defines a single data source and the target table or tables into which data loads. A data source includes the location of the source and filters on the source to define the specific data set to load. This tutorial loads data from two data sources, where each source is located in a directory on the same S3 bucket. First, inspect the data that you plan to load. Each document has a JSON format similar to this example. ```json JSON theme={null} {"id": 1, "user_id": 1, "product_id": 14, "subtotal": 37.65, "tax": 2.07, "total": 39.72, "discount": null, "created_at": "2019-02-11T21:40:27.892Z", "quantity": 2} {"id": 2, "user_id": 1, "product_id": 123, "subtotal": 110.93, "tax": 6.1, "total": 117.03, "discount": null, "created_at": "2018-05-15T08:04:04.580Z", "quantity": 3} ``` Inspecting the file with Pandas, you can see this schema with details. ```none Parquet Schema Description theme={null} # Column Non-Null Count Dtype --- ------ -------------- ----- 0 id 18760 non-null int64 1 user_id 18760 non-null int64 2 product_id 18760 non-null int64 3 subtotal 18760 non-null float64 4 tax 18760 non-null float64 5 total 18760 non-null float64 6 discount 1915 non-null float64 7 created_at 18760 non-null datetime64[ns, UTC] 8 quantity 18760 non-null int64 ``` In this case, Ocient automatically transforms the data to the target columns using some sensible conventions. In other cases, loads require some transformation. Most transformations are identical to functions that already exist in the SQL syntax of the Ocient System. Prior to creating a pipeline, you can use the `PREVIEW PIPELINE` SQL statement to create your pipeline iteratively. This statement returns a result set that shows the final values that would be loaded but does not load the data into the target table. Preview the `orders_pipeline` pipeline for the `orders` data set from your database connection prompt. Use the S3 data source with endpoint `https://s3.us-east-1.amazonaws.com`, bucket `ocient-docs`, and filter `metabase_samples/parquet/orders.parquet`. Specify the `parquet` format. Load the data into the `public.orders` table. The `SELECT` part of the SQL statement maps the fields in the Parquet file to the target columns in the created table. In this example, limit the result set to the first five records in the data source. ```sql SQL theme={null} PREVIEW PIPELINE orders_pipeline SOURCE S3 ENDPOINT 'https://s3.us-east-1.amazonaws.com' BUCKET 'ocient-docs' FILTER 'metabase_samples/parquet/orders.parquet' LIMIT 5 EXTRACT FORMAT parquet INTO public.orders SELECT $id as id, $user_id as user_id, $product_id as product_id, $subtotal as subtotal, $tax as tax, $total as total, $discount as discount, $created_at as created_at, $quantity as quantity; ``` *Output* ```sql SQL theme={null} id user_id product_id subtotal tax total discount created_at quantity ----------------------------------------------------------------------------------------------------------------------------------------------------------------- 1 1 14 37.65 2.07 39.72 NULL 2019-02-11 21:40:27.892000000 2 8 1 123 110.93 6.1 117.03 NULL 2018-05-15 08:04:04.580000000 3 8 1 105 52.72 2.9 49.2 6.42 2019-12-06 22:22:48.544000000 2 8 1 94 109.22 6.01 115.23 NULL 2019-08-22 16:30:42.392000000 6 8 1 132 127.88 7.03 134.91 NULL 2018-10-10 03:34:47.309000000 5 Fetched 5 rows ``` With this preview, you can confirm that the results of the pipeline match your requirements. If there is an issue, you can update the statement and run the `PREVIEW PIPELINE` statement again until it meets your needs. Next, create the pipeline named `orders_pipeline` for the `orders` data set from your database connection prompt. The pipeline has three main sections: * `SOURCE` — Loads data from S3. Set the S3 endpoint, bucket name, and filter for the Parquet files. * `EXTRACT` — Sets the format to Parquet. * `INTO ... SELECT` — Targets the `public.orders` table and selects the chosen fields from the Parquet records. In this case, all data is available at the top level of the Parquet records, so the example references the fields by the attribute name (e.g., `$id`, `$user_id`, etc.). For nested data, reference the nested fields using dot notation (e.g., `$order.user.first_name`). Each field maps to a target column using the `as` syntax. ```sql SQL theme={null} CREATE PIPELINE orders_pipeline SOURCE S3 ENDPOINT 'https://s3.us-east-1.amazonaws.com' BUCKET 'ocient-docs' FILTER 'metabase_samples/parquet/orders.parquet' EXTRACT FORMAT parquet INTO public.orders SELECT $id as id, $user_id as user_id, $product_id as product_id, $subtotal as subtotal, $tax as tax, $total as total, $discount as discount, $created_at as created_at, $quantity as quantity; ``` After you successfully create this pipeline, execute the `START PIPELINE` SQL statement to start the load. ```sql SQL theme={null} START PIPELINE orders_pipeline; ``` ### Step 4: Observe the Load Progress With your pipeline running, data begins to load immediately from the S3 files that you defined. If there are many files in each file group, the load process first sorts the files into batches, partitions them for parallel processing, and assigns them to Loader Nodes. You can check the pipeline status and progress by querying the `information_schema.pipeline_status` system catalog table or executing the `SHOW PIPELINE_STATUS` SQL statement. ```sql SQL theme={null} SHOW PIPELINE_STATUS; ``` *Output* ```sql SQL theme={null} database_name pipeline_name table_names status status_message percent_complete duration_seconds files_processed files_failed files_remaining records_processed records_loaded records_failed -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- metabase orders_pipeline ["public.orders"] RUNNING Started processing pipeline orders_pipeline 0.0 2.025824 0 0 1 0 0 0 ``` After the status of the pipeline changes to `COMPLETED`, all data is available in the target table. After a few seconds, the data is available for query in the `public.orders` table. ```sql SQL theme={null} SELECT COUNT(*) FROM public.orders; ``` *Output* ```sql SQL theme={null} count(*) -------------------- 18760 ``` You can drop the pipeline with the `DROP PIPELINE orders_pipeline;` SQL statement. Execution of this statement leaves the data in your target table, but removes metadata about the pipeline execution from the system. ## Related Links [Data Pipelines Reference](/data-pipelines) [Load JSON Data](/data-formats-for-data-pipelines#load-json-data) [Data Types for Data Pipelines](/data-types-for-data-pipelines) [Transform Data in Data Pipelines](/transform-data-in-data-pipelines) [Manage Errors in Data Pipelines](/manage-errors-in-data-pipelines)