How to load data from Snowflake to DynamoDB

Learn how to use Airbyte to synchronize your Snowflake data into DynamoDB within minutes.

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Set up a Snowflake connector in Airbyte

Connect to Snowflake or one of 400+ pre-built or 10,000+ custom connectors through simple account authentication.

Set up DynamoDB for your extracted Snowflake data

Select DynamoDB where you want to import data from your Snowflake source to. You can also choose other cloud data warehouses, databases, data lakes, vector databases, or any other supported Airbyte destinations.

Configure the Snowflake to DynamoDB in Airbyte

This includes selecting the data you want to extract - streams and columns -, the sync frequency, where in the destination you want that data to be loaded.

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TL;DR

set up Snowflake as a source connector (using Auth, or usually an API key)
set up DynamoDB as a destination connector
define which data you want to transfer and how frequently

You can choose to self-host the pipeline using Airbyte Open Source or have it managed for you with Airbyte Cloud.

This tutorial’s purpose is to show you how.

What is Snowflake

Snowflake Data Cloud is a cloud-based data warehousing and analytics platform that allows organizations to store, manage, and analyze large amounts of data in a secure and scalable manner. It provides a single, integrated platform for data storage, processing, and analysis, eliminating the need for multiple tools and systems. Snowflake Data Cloud is built on a unique architecture that separates compute and storage, allowing users to scale up or down as needed without affecting performance. It also offers a range of features such as data sharing, data governance, and machine learning capabilities, making it a comprehensive solution for modern data management and analytics.

What is DynamoDB

Amazon DynamoDB is a fully managed proprietary NoSQL database service that supports key–value and document data structures and is offered by Amazon.com as part of the Amazon Web Services portfolio. DynamoDB exposes a similar data model to and derives its name from Dynamo, but has a different underlying implementation.

Integrate Snowflake with DynamoDB in minutes

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Prerequisites

A Snowflake account to transfer your customer data automatically from.
A DynamoDB account.
An active Airbyte Cloud account, or you can also choose to use Airbyte Open Source locally. You can follow the instructions to set up Airbyte on your system using docker-compose.

Airbyte is an open-source data integration platform that consolidates and streamlines the process of extracting and loading data from multiple data sources to data warehouses. It offers pre-built connectors, including Snowflake and DynamoDB, for seamless data migration.

When using Airbyte to move data from Snowflake to DynamoDB, it extracts data from Snowflake using the source connector, converts it into a format DynamoDB can ingest using the provided schema, and then loads it into DynamoDB via the destination connector. This allows businesses to leverage their Snowflake data for advanced analytics and insights within DynamoDB, simplifying the ETL process and saving significant time and resources.

Step 1: Set up Snowflake as a source connector

1. First, you need to have a Snowflake Data Cloud account and the necessary credentials to access it.

2. Once you have the credentials, go to the Airbyte dashboard and click on "Sources" on the left-hand side of the screen.

3. Click on the "Create a new source" button and select "Snowflake Data Cloud" from the list of available sources.

4. Enter a name for your Snowflake Data Cloud source and click on "Next".

5. In the "Connection" tab, enter the following information:
- Account name: the name of your Snowflake account
- Username: your Snowflake username
- Password: your Snowflake password
- Warehouse: the name of the warehouse you want to use
- Database: the name of the database you want to use
- Schema: the name of the schema you want to use

6. Click on "Test connection" to make sure that the connection is successful.

7. If the connection is successful, click on "Next" to proceed to the "Configuration" tab.

8. In the "Configuration" tab, select the tables or views that you want to replicate and configure any necessary settings.

9. Click on "Create source" to save your Snowflake Data Cloud source and start replicating data.

Step 2: Set up DynamoDB as a destination connector

1. Open the Airbyte platform and navigate to the "Destinations" tab on the left-hand side of the screen.
2. Scroll down until you find the "DynamoDB" connector and click on it.
3. Click on the "Create new connection" button.
4. Enter a name for your connection and click on the "Next" button.
5. Enter your AWS access key ID and secret access key in the appropriate fields.
6. Enter the name of the DynamoDB table you want to connect to.
7. Choose the region where your DynamoDB table is located.
8. Click on the "Test connection" button to ensure that your credentials are correct and that the connection is successful.
9. If the test is successful, click on the "Create connection" button to save your settings.
10. You can now use the DynamoDB destination connector to transfer data from your source to your DynamoDB table.

Step 3: Set up a connection to sync your Snowflake data to DynamoDB

Once you've successfully connected Snowflake as a data source and DynamoDB as a destination in Airbyte, you can set up a data pipeline between them with the following steps:

Create a new connection: On the Airbyte dashboard, navigate to the 'Connections' tab and click the '+ New Connection' button.
Choose your source: Select Snowflake from the dropdown list of your configured sources.
Select your destination: Choose DynamoDB from the dropdown list of your configured destinations.
Configure your sync: Define the frequency of your data syncs based on your business needs. Airbyte allows both manual and automatic scheduling for your data refreshes.
Select the data to sync: Choose the specific Snowflake objects you want to import data from towards DynamoDB. You can sync all data or select specific tables and fields.
Select the sync mode for your streams: Choose between full refreshes or incremental syncs (with deduplication if you want), and this for all streams or at the stream level. Incremental is only available for streams that have a primary cursor.
Test your connection: Click the 'Test Connection' button to make sure that your setup works. If the connection test is successful, save your configuration.
Start the sync: If the test passes, click 'Set Up Connection'. Airbyte will start moving data from Snowflake to DynamoDB according to your settings.

Remember, Airbyte keeps your data in sync at the frequency you determine, ensuring your DynamoDB data warehouse is always up-to-date with your Snowflake data.

Use Cases to transfer your Snowflake data to DynamoDB

Integrating data from Snowflake to DynamoDB provides several benefits. Here are a few use cases:

Advanced Analytics: DynamoDB’s powerful data processing capabilities enable you to perform complex queries and data analysis on your Snowflake data, extracting insights that wouldn't be possible within Snowflake alone.
Data Consolidation: If you're using multiple other sources along with Snowflake, syncing to DynamoDB allows you to centralize your data for a holistic view of your operations, and to set up a change data capture process so you never have any discrepancies in your data again.
Historical Data Analysis: Snowflake has limits on historical data. Syncing data to DynamoDB allows for long-term data retention and analysis of historical trends over time.
Data Security and Compliance: DynamoDB provides robust data security features. Syncing Snowflake data to DynamoDB ensures your data is secured and allows for advanced data governance and compliance management.
Scalability: DynamoDB can handle large volumes of data without affecting performance, providing an ideal solution for growing businesses with expanding Snowflake data.
Data Science and Machine Learning: By having Snowflake data in DynamoDB, you can apply machine learning models to your data for predictive analytics, customer segmentation, and more.
Reporting and Visualization: While Snowflake provides reporting tools, data visualization tools like Tableau, PowerBI, Looker (Google Data Studio) can connect to DynamoDB, providing more advanced business intelligence options. If you have a Snowflake table that needs to be converted to a DynamoDB table, Airbyte can do that automatically.

Wrapping Up

To summarize, this tutorial has shown you how to:

Configure a Snowflake account as an Airbyte data source connector.
Configure DynamoDB as a data destination connector.
Create an Airbyte data pipeline that will automatically be moving data directly from Snowflake to DynamoDB after you set a schedule

With Airbyte, creating data pipelines take minutes, and the data integration possibilities are endless. Airbyte supports the largest catalog of API tools, databases, and files, among other sources. Airbyte's connectors are open-source, so you can add any custom objects to the connector, or even build a new connector from scratch without any local dev environment or any data engineer within 10 minutes with the no-code connector builder.

We look forward to seeing you make use of it! We invite you to join the conversation on our community Slack Channel, or sign up for our newsletter. You should also check out other Airbyte tutorials, and Airbyte’s content hub!

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How to Sync Snowflake to DynamoDB Manually

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FAQs

What is ETL?

ETL, an acronym for Extract, Transform, Load, is a vital data integration process. It involves extracting data from diverse sources, transforming it into a usable format, and loading it into a database, data warehouse or data lake. This process enables meaningful data analysis, enhancing business intelligence.

What is Snowflake?

What data can you extract from Snowflake?

Snowflake Data Cloud provides access to a wide range of data types, including:

1. Structured Data: This includes data that is organized in a specific format, such as tables, columns, and rows. Examples of structured data include customer information, financial data, and inventory records.
2. Semi-Structured Data: This type of data is partially organized and may not fit into a traditional relational database structure. Examples of semi-structured data include JSON, XML, and CSV files.
3. Unstructured Data: This includes data that does not have a specific format or organization, such as text documents, images, and videos.
4. Time-Series Data: This type of data is organized based on time stamps and is commonly used in industries such as finance, healthcare, and manufacturing.
5. Geospatial Data: This includes data that is related to geographic locations, such as maps, GPS coordinates, and satellite imagery.
6. Machine Learning Data: This type of data is used to train machine learning models and includes features and labels that are used to predict outcomes.

Overall, Snowflake Data Cloud provides access to a wide range of data types, making it a versatile tool for data analysis and management.

How do I transfer data from Snowflake?

This can be done by building a data pipeline manually, usually a Python script (you can leverage a tool as Apache Airflow for this). This process can take more than a full week of development. Or it can be done in minutes on Airbyte in three easy steps:
1. Set up Snowflake Data Cloud to DynamoDB as a source connector (using Auth, or usually an API key)
2. Choose a destination (more than 50 available destination databases, data warehouses or lakes) to sync data too and set it up as a destination connector
3. Define which data you want to transfer from Snowflake Data Cloud to DynamoDB and how frequently
You can choose to self-host the pipeline using Airbyte Open Source or have it managed for you with Airbyte Cloud.

What is ELT?

ELT, standing for Extract, Load, Transform, is a modern take on the traditional ETL data integration process. In ELT, data is first extracted from various sources, loaded directly into a data warehouse, and then transformed. This approach enhances data processing speed, analytical flexibility and autonomy.

Difference between ETL and ELT?

ETL and ELT are critical data integration strategies with key differences. ETL (Extract, Transform, Load) transforms data before loading, ideal for structured data. In contrast, ELT (Extract, Load, Transform) loads data before transformation, perfect for processing large, diverse data sets in modern data warehouses. ELT is becoming the new standard as it offers a lot more flexibility and autonomy to data analysts.

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Databases

Warehouses and Lakes

How to load data from Snowflake to DynamoDB

Learn how to use Airbyte to synchronize your Snowflake data into DynamoDB within minutes.

Try Airbyte Cloud free

Deploy Airbyte Open Source

Step 1: Setup Snowflake

Step 2: Setup DynamoDB

Step 3: Setup a connection Use Cases Wrapping Up

Example H2

Example H3

Sync data between Snowflake and DynamoDB

Get your data syncing in minutes

Try Airbyte free

TL;DR

set up Snowflake as a source connector (using Auth, or usually an API key)
set up DynamoDB as a destination connector
define which data you want to transfer and how frequently

You can choose to self-host the pipeline using Airbyte Open Source or have it managed for you with Airbyte Cloud.

This tutorial’s purpose is to show you how.

What is Snowflake

What is DynamoDB

Integrate Snowflake with DynamoDB in minutes

Try for free now

Prerequisites

A Snowflake account to transfer your customer data automatically from.
A DynamoDB account.
An active Airbyte Cloud account, or you can also choose to use Airbyte Open Source locally. You can follow the instructions to set up Airbyte on your system using docker-compose.