How to load data from Everhour to AWS Datalake

Learn how to use Airbyte to synchronize your Everhour data into AWS Datalake within minutes.

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Set up a Everhour connector in Airbyte

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

Set up AWS Datalake for your extracted Everhour data

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

Configure the Everhour to AWS Datalake 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 Everhour as a source connector (using Auth, or usually an API key)
set up AWS Datalake 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 Everhour

Everhour is a time tracking and project management tool that helps businesses and teams to manage their time more efficiently. It integrates with popular project management tools like Asana, Trello, and Basecamp, allowing users to track time spent on tasks and projects directly from those platforms. Everhour also offers features like budget tracking, invoicing, and reporting, giving businesses a comprehensive view of their time and project management. With Everhour, teams can easily collaborate, manage their workload, and stay on top of deadlines, ultimately improving productivity and profitability.

What is AWS Datalake

An AWS Data Lake is a centralized repository that allows you to store all your structured and unstructured data at any scale. It is designed to handle massive amounts of data from various sources, such as databases, applications, IoT devices, and more. With AWS Data Lake, you can easily ingest, store, catalog, process, and analyze data using a wide range of AWS services like Amazon S3, Amazon Athena, AWS Glue, and Amazon EMR. This allows you to build data lakes for machine learning, big data analytics, and data warehousing workloads. AWS Data Lake provides a secure, scalable, and cost-effective solution for managing your organization's data.

Integrate Everhour with AWS Datalake in minutes

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Prerequisites

A Everhour account to transfer your customer data automatically from.
A AWS Datalake 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 Everhour and AWS Datalake, for seamless data migration.

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

Step 1: Set up Everhour as a source connector

1. First, log in to your Everhour account and navigate to the API Access page.
2. Generate a new API key by clicking on the ""Generate new API key"" button.
3. Copy the API key to your clipboard.
4. Open the Airbyte platform and navigate to the ""Sources"" tab.
5. Click on the ""New Source"" button and select ""Everhour"" from the list of available connectors.
6. Enter a name for your Everhour source connector and paste the API key you copied earlier into the appropriate field.
7. Click on the ""Test"" button to ensure that the connection is successful.
8. If the test is successful, click on the ""Create"" button to save your Everhour source connector.
9. You can now use your Everhour source connector to extract data from your Everhour account and integrate it with other tools and platforms.

‍

Step 2: Set up AWS Datalake as a destination connector

1. Log in to your AWS account and navigate to the AWS Management Console.
2. Click on the S3 service and create a new bucket where you will store your data.
3. Create an IAM user with the necessary permissions to access the S3 bucket. Make sure to save the access key and secret key.
4. Open Airbyte and navigate to the Destinations tab.
5. Select the AWS Datalake destination connector and click on "Create new connection".
6. Enter a name for your connection and paste the access key and secret key you saved earlier.
7. Enter the name of the S3 bucket you created in step 2 and select the region where it is located.
8. Choose the format in which you want your data to be stored in the S3 bucket (e.g. CSV, JSON, Parquet).
9. Configure any additional settings, such as compression or encryption, if necessary.
10. Test the connection to make sure it is working properly.
11. Save the connection and start syncing your data to the AWS Datalake.

Step 3: Set up a connection to sync your Everhour data to AWS Datalake

Once you've successfully connected Everhour as a data source and AWS Datalake 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 Everhour from the dropdown list of your configured sources.
Select your destination: Choose AWS Datalake 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 Everhour objects you want to import data from towards AWS Datalake. 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 Everhour to AWS Datalake according to your settings.

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

Use Cases to transfer your Everhour data to AWS Datalake

Integrating data from Everhour to AWS Datalake provides several benefits. Here are a few use cases:

Advanced Analytics: AWS Datalake’s powerful data processing capabilities enable you to perform complex queries and data analysis on your Everhour data, extracting insights that wouldn't be possible within Everhour alone.
Data Consolidation: If you're using multiple other sources along with Everhour, syncing to AWS Datalake 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: Everhour has limits on historical data. Syncing data to AWS Datalake allows for long-term data retention and analysis of historical trends over time.
Data Security and Compliance: AWS Datalake provides robust data security features. Syncing Everhour data to AWS Datalake ensures your data is secured and allows for advanced data governance and compliance management.
Scalability: AWS Datalake can handle large volumes of data without affecting performance, providing an ideal solution for growing businesses with expanding Everhour data.
Data Science and Machine Learning: By having Everhour data in AWS Datalake, you can apply machine learning models to your data for predictive analytics, customer segmentation, and more.
Reporting and Visualization: While Everhour provides reporting tools, data visualization tools like Tableau, PowerBI, Looker (Google Data Studio) can connect to AWS Datalake, providing more advanced business intelligence options. If you have a Everhour table that needs to be converted to a AWS Datalake table, Airbyte can do that automatically.

Wrapping Up

To summarize, this tutorial has shown you how to:

Configure a Everhour account as an Airbyte data source connector.
Configure AWS Datalake as a data destination connector.
Create an Airbyte data pipeline that will automatically be moving data directly from Everhour to AWS Datalake 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 Everhour to AWS Datalake Manually

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Step 8:

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 Everhour?

What data can you extract from Everhour?

Everhour's API provides access to a wide range of data related to time tracking and project management. The following are the categories of data that can be accessed through Everhour's API:

1. Time tracking data: This includes data related to the time spent on tasks, projects, and clients.

2. Project management data: This includes data related to projects, tasks, and subtasks, such as their status, due dates, and assignees.

3. User data: This includes data related to users, such as their name, email address, and role.

4. Billing data: This includes data related to billing, such as the amount billed, the currency used, and the payment status.

5. Reporting data: This includes data related to reports, such as the type of report, the date range, and the data included in the report.

6. Integration data: This includes data related to integrations with other tools, such as the name of the integration, the status, and the configuration settings.

Overall, Everhour's API provides a comprehensive set of data that can be used to track time, manage projects, and analyze performance.

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How do I transfer data from Everhour?

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 Everhour to AWS Datalake 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 Everhour to AWS Datalake 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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Warehouses and Lakes

Finance & Ops Analytics

How to load data from Everhour to AWS Datalake

Learn how to use Airbyte to synchronize your Everhour data into AWS Datalake within minutes.

Try Airbyte Cloud free

Deploy Airbyte Open Source

Step 1: Setup Everhour

Step 2: Setup AWS Datalake

Step 3: Setup a connection Use Cases Wrapping Up

Example H2

Example H3

Sync data between Everhour and AWS Datalake

Get your data syncing in minutes

Try Airbyte free

TL;DR

set up Everhour as a source connector (using Auth, or usually an API key)
set up AWS Datalake 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 Everhour

What is AWS Datalake

Integrate Everhour with AWS Datalake in minutes

Try for free now

Prerequisites

A Everhour account to transfer your customer data automatically from.
A AWS Datalake 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 Everhour as a source connector

‍

Step 2: Set up AWS Datalake as a destination connector

Step 3: Set up a connection to sync your Everhour data to AWS Datalake

Once you've successfully connected Everhour as a data source and AWS Datalake 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 Everhour from the dropdown list of your configured sources.
Select your destination: Choose AWS Datalake 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 Everhour objects you want to import data from towards AWS Datalake. 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 Everhour to AWS Datalake according to your settings.

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

Use Cases to transfer your Everhour data to AWS Datalake

Integrating data from Everhour to AWS Datalake provides several benefits. Here are a few use cases:

Advanced Analytics: AWS Datalake’s powerful data processing capabilities enable you to perform complex queries and data analysis on your Everhour data, extracting insights that wouldn't be possible within Everhour alone.
Data Consolidation: If you're using multiple other sources along with Everhour, syncing to AWS Datalake 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: Everhour has limits on historical data. Syncing data to AWS Datalake allows for long-term data retention and analysis of historical trends over time.
Data Security and Compliance: AWS Datalake provides robust data security features. Syncing Everhour data to AWS Datalake ensures your data is secured and allows for advanced data governance and compliance management.
Scalability: AWS Datalake can handle large volumes of data without affecting performance, providing an ideal solution for growing businesses with expanding Everhour data.
Data Science and Machine Learning: By having Everhour data in AWS Datalake, you can apply machine learning models to your data for predictive analytics, customer segmentation, and more.
Reporting and Visualization: While Everhour provides reporting tools, data visualization tools like Tableau, PowerBI, Looker (Google Data Studio) can connect to AWS Datalake, providing more advanced business intelligence options. If you have a Everhour table that needs to be converted to a AWS Datalake table, Airbyte can do that automatically.