How to load data from Parquet File to DynamoDB

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

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Set up a Parquet File connector in Airbyte

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

Set up DynamoDB for your extracted Parquet File data

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

Configure the Parquet File 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 Parquet File 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 Parquet File

Parquet File is a columnar storage file format that is designed to store and process large amounts of data efficiently. It is an open-source project that was developed by Cloudera and Twitter. Parquet File is optimized for use with Hadoop and other big data processing frameworks, and it is designed to work well with both structured and unstructured data. The format is highly compressed, which makes it ideal for storing and processing large datasets. Parquet File is also designed to be highly scalable, which means that it can be used to store and process data across multiple nodes in a distributed computing environment.

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 Parquet File with DynamoDB in minutes

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Prerequisites

A Parquet File 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 Parquet File and DynamoDB, for seamless data migration.

When using Airbyte to move data from Parquet File to DynamoDB, it extracts data from Parquet File 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 Parquet File data for advanced analytics and insights within DynamoDB, simplifying the ETL process and saving significant time and resources.

Step 1: Set up Parquet File as a source connector

1. Open the Airbyte dashboard and click on "Sources" on the left-hand side of the screen.
2. Click on the "Create Connection" button and select "Parquet File" from the list of available connectors.
3. Enter a name for your connection and click on "Next".
4. In the "Configuration" tab, enter the path to your Parquet file in the "File Path" field.
5. If your Parquet file is password-protected, enter the password in the "Password" field.
6. If your Parquet file is encrypted, select the appropriate encryption type from the "Encryption Type" dropdown menu and enter the encryption key in the "Encryption Key" field.
7. Click on "Test Connection" to ensure that your credentials are correct and that Airbyte can connect to your Parquet file.
8. If the test is successful, click on "Create" to save your connection.
9. You can now use this connection to create a new Airbyte pipeline and start syncing data from your Parquet file to your destination.

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 Parquet File data to DynamoDB

Once you've successfully connected Parquet File 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 Parquet File 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 Parquet File 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 Parquet File 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 Parquet File data.

Use Cases to transfer your Parquet File data to DynamoDB

Integrating data from Parquet File 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 Parquet File data, extracting insights that wouldn't be possible within Parquet File alone.
Data Consolidation: If you're using multiple other sources along with Parquet File, 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: Parquet File 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 Parquet File 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 Parquet File data.
Data Science and Machine Learning: By having Parquet File data in DynamoDB, you can apply machine learning models to your data for predictive analytics, customer segmentation, and more.
Reporting and Visualization: While Parquet File 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 Parquet File 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 Parquet File 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 Parquet File 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 Parquet File 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 Parquet File?

What data can you extract from Parquet File?

Parquet File's API gives access to various types of data, including:

• Structured data: Parquet files can store structured data in a columnar format, making it easy to query and analyze large datasets.
• Semi-structured data: Parquet files can also store semi-structured data, such as JSON or XML, allowing for more flexibility in data storage.
• Unstructured data: Parquet files can store unstructured data, such as text or binary data, making it possible to store a wide range of data types in a single file.
• Big data: Parquet files are designed for big data applications, allowing for efficient storage and processing of large datasets.
• Machine learning data: Parquet files are commonly used in machine learning applications, as they can store large amounts of data in a format that is optimized for processing by machine learning algorithms.

Overall, Parquet File's API provides access to a wide range of data types, making it a versatile tool for data storage and analysis in a variety of applications.

How do I transfer data from Parquet File?

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 Parquet File 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 Parquet File 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

Files

How to load data from Parquet File to DynamoDB

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

Try Airbyte Cloud free

Deploy Airbyte Open Source

Step 1: Setup Parquet File

Step 2: Setup DynamoDB

Step 3: Setup a connection Use Cases Wrapping Up

Example H2

Example H3

Sync data between Parquet File and DynamoDB

Get your data syncing in minutes

Try Airbyte free

TL;DR

set up Parquet File 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 Parquet File

What is DynamoDB

Integrate Parquet File with DynamoDB in minutes

Try for free now

Prerequisites

A Parquet File 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.

Step 1: Set up Parquet File as a source connector

Step 2: Set up DynamoDB as a destination connector

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

Once you've successfully connected Parquet File 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 Parquet File 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 Parquet File 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 Parquet File 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 Parquet File data.

Use Cases to transfer your Parquet File data to DynamoDB

Integrating data from Parquet File 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 Parquet File data, extracting insights that wouldn't be possible within Parquet File alone.
Data Consolidation: If you're using multiple other sources along with Parquet File, 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: Parquet File 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 Parquet File 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 Parquet File data.
Data Science and Machine Learning: By having Parquet File data in DynamoDB, you can apply machine learning models to your data for predictive analytics, customer segmentation, and more.
Reporting and Visualization: While Parquet File 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 Parquet File table that needs to be converted to a DynamoDB table, Airbyte can do that automatically.