How to Insert and Parse JSON into Snowflake: Data Engineering Project Part 2
Table of Contents:
- Introduction
- Data Extraction and Pre-processing
2.1 Extracting data from PredictIt
2.2 Inserting data into Snowflake
2.3 Basic parsing of JSON files
- Creating Tables for Market and Contract Information
3.1 Storing market information
3.2 Tracking contract changes over time
- Using Snowflake Objects for Data Analysis
4.1 Snowflake stage
4.2 Snowflake tasks
4.3 Storage integration
- Automating Data Loading with Tasks
5.1 Creating a table for raw data
5.2 Automating the data load process
5.3 Managing dependencies and re-running tasks
- Parsing JSON and Inserting Data into Tables
6.1 Flattening the JSON data
6.2 Filtering duplicate data
6.3 Inserting data into the stage table
- Creating Dependencies and Restarting Tasks
7.1 Creating dependencies between tasks
7.2 Restarting suspended tasks
- Conclusion
Introduction
Hey there, guys! Welcome back to another video with me, Ben Rogan, also known as the Seattle data guy. In this video, we'll be diving into part two of my data engineering project. If You remember from the previous video, we extracted data from PredictIt and now we're going to take that data and insert it into Snowflake. This video will primarily focus on the pre-processing steps involved. So, let's dive in!
Data Extraction and Pre-processing
In order to get started, let's refresh our memory on what we're trying to achieve here. The data we extracted consists of two entities: markets and contracts. Markets represent the parent information, while contracts represent the child information. Our goal is to break down this data into two separate tables: one for markets and another for contracts. However, we also want to track the changes in contracts over time to analyze trends and Patterns. To accomplish this, we'll Create two tables: a market table and a contract table.
Creating Tables for Market and Contract Information
Now that we have a clear understanding of our goal, let's create the necessary tables. We'll start by creating a table to store the raw predicted data. This table will contain all the extracted data from PredictIt. We'll use the COPY INTO
statement to load the data into the table. Snowflake allows us to easily load data and automatically handle any duplicate files, making the process more efficient.
Using Snowflake Objects for Data Analysis
Snowflake provides various objects that we can use for our data analysis. One of these objects is the stage, which allows us to reference data sources through storage integration. Storage integration helps secure the data and is considered a best practice in Snowflake. We have already created the necessary stage and storage integration. Now, we can start pulling information from it.
Automating Data Loading with Tasks
While we have manually loaded the data so far, we can automate this process using tasks in Snowflake. Tasks help schedule and execute SQL statements at specific intervals. We can create a task to load the data into the table automatically. Snowflake tasks are easy to set up, requiring only a few lines of code to define the task and its schedule. However, managing dependencies and re-running tasks can be more complex in Snowflake compared to other orchestration systems like Airflow.
Parsing JSON and Inserting Data into Tables
Next, we need to parse the JSON data and insert it into the respective tables. We start by flattening the JSON data using the LATERAL FLATTEN
statement. This allows us to extract the specific sections we need, such as market information. We then further parse the JSON to extract the ID, name, short name, and URL for each market. To avoid duplicating data, we filter out existing IDs from the staging table using a LEFT JOIN
. This ensures that only new markets are included in the table.
Creating Dependencies and Restarting Tasks
To create dependencies between tasks, we modify the task's SQL statement and specify a dependency on a previous task. In this case, we make the market task dependent on the raw data task. We also need to ensure that the tasks are not suspended and are ready to run. By explicitly resuming the tasks, we can ensure they start running as expected.
Conclusion
In this video, we covered the steps involved in extracting data from PredictIt, pre-processing it, and loading it into Snowflake. We also explored automation using Snowflake tasks and managing dependencies between tasks. Now that we have our data ready, we can move forward and use it to answer questions and create visualizations, perhaps using tools like Tableau. Thank you for watching, and I'll see you all next time!
Highlights:
- Extract data from PredictIt and insert it into Snowflake for analysis.
- Create tables for market and contract information.
- Use Snowflake objects like stages and tasks for data processing and automation.
- Parse JSON data and insert it into tables.
- Manage dependencies and re-run tasks in Snowflake.
FAQ:
Q: What is Snowflake?
A: Snowflake is a cloud-based data warehousing platform that allows for efficient storage and analysis of large datasets.
Q: How can tasks be automated in Snowflake?
A: Tasks in Snowflake can be automated by defining a schedule and specifying the SQL statements to execute at specific intervals.
Q: Can Snowflake handle duplicate files during data loading?
A: Yes, Snowflake automatically handles duplicate files and provides a detailed report on the loaded data.
Resources:
- PredictIt: [Website URL]
- Snowflake: [website URL]
- Tableau: [website URL]