How to load data from My Hours to Kafka

Learn how to use Airbyte to synchronize your My Hours data into Kafka within minutes.

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Set up a My Hours connector in Airbyte

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

Set up Kafka for your extracted My Hours data

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

Configure the My Hours to Kafka 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 My Hours as a source connector (using Auth, or usually an API key)
set up Kafka 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 My Hours

My Hours was launched back in 2002 and it is a cloud-based time-tracking solution best suited for small teams and freelancers. Since then My Hours has been rewritten twice to meet the growing demands and it is a product of Spica, a company headquartered in Ljubljana with 100+ employees. The users of My Hours can start time tracking on unlimited projects and tasks in seconds which easily generates insightful reports and create invoices.

What is Kafka

A communication solutions agency, Kafka is a cloud-based / on-prem distributed system offering social media services, public relations, and events. For event streaming, three main functionalities are available: the ability to (1) subscribe to (read) and publish (write) streams of events, (2) store streams of events indefinitely, durably, and reliably, and (3) process streams of events in either real-time or retrospectively. Kafka offers these capabilities in a secure, highly scalable, and elastic manner.

Integrate My Hours with Kafka in minutes

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Prerequisites

A My Hours account to transfer your customer data automatically from.
A Kafka 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 My Hours and Kafka, for seamless data migration.

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

Step 1: Set up My Hours as a source connector

1. First, navigate to the My Hours source connector page on Airbyte.com.
2. Click on the "Setup" button to begin configuring the connector.
3. Enter your My Hours API key in the "API Key" field. You can find your API key by logging into your My Hours account and navigating to the "API" section of the settings.
4. Next, enter your My Hours email address in the "Email" field.
5. In the "Workspace ID" field, enter the ID of the workspace you want to connect to Airbyte. You can find this ID by navigating to the workspace in My Hours and looking at the URL. The ID will be the number at the end of the URL.
6. Finally, click on the "Test" button to ensure that the connection is working properly. If the test is successful, click on the "Save" button to save your credentials and complete the setup process.
7. You can now use the My Hours source connector to extract data from your My Hours workspace and integrate it with other tools and platforms through Airbyte.

Step 2: Set up Kafka as a destination connector

1. First, you need to have an Apache Kafka destination connector installed on your system. If you don't have it, you can download it from the Apache Kafka website.
2. Once you have the Apache Kafka destination connector installed, you need to create a new connection in Airbyte. To do this, go to the Connections tab and click on the "New Connection" button. 3. In the "New Connection" window, select "Apache Kafka" as the destination connector and enter the required connection details, such as the Kafka broker URL, topic name, and authentication credentials.
4. After entering the connection details, click on the "Test Connection" button to ensure that the connection is working properly.
5. If the connection test is successful, click on the "Save" button to save the connection.
6. Once the connection is saved, you can create a new pipeline in Airbyte and select the Apache Kafka destination connector as the destination for your data.
7. In the pipeline configuration, select the connection you created in step 3 as the destination connection.
8. Configure the pipeline to map the source data to the appropriate Kafka topic and fields.
9. Once the pipeline is configured, you can run it to start sending data to your Apache Kafka destination.

Step 3: Set up a connection to sync your My Hours data to Kafka

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

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

Use Cases to transfer your My Hours data to Kafka

Integrating data from My Hours to Kafka provides several benefits. Here are a few use cases:

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

Wrapping Up

To summarize, this tutorial has shown you how to:

Configure a My Hours account as an Airbyte data source connector.
Configure Kafka as a data destination connector.
Create an Airbyte data pipeline that will automatically be moving data directly from My Hours to Kafka 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 My Hours to Kafka 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 My Hours?

What data can you extract from My Hours?

My Hours' API provides access to a variety of data related to time tracking and project management. The following are the categories of data that can be accessed through the API:

1. Time tracking data: This includes information about the time spent on tasks, projects, and clients. It includes start and end times, duration, and any notes or comments associated with the time entry.
2. Project data: This includes information about the projects being worked on, such as project name, description, status, and associated tasks.
3. Task data: This includes information about the individual tasks within a project, such as task name, description, status, and associated time entries.
4. Client data: This includes information about the clients being worked with, such as client name, contact information, and associated projects.
5. User data: This includes information about the users of the My Hours platform, such as user name, email address, and associated time entries, projects, and tasks.

Overall, the My Hours API provides a comprehensive set of data that can be used to analyze and optimize time tracking and project management processes.

How do I transfer data from My Hours?

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 My Hours to Kafka 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 My Hours to Kafka 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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How to load data from My Hours to Kafka

Learn how to use Airbyte to synchronize your My Hours data into Kafka within minutes.

Try Airbyte Cloud free

Deploy Airbyte Open Source

Step 1: Setup My Hours

Step 2: Setup Kafka

Step 3: Setup a connection Use Cases Wrapping Up

Example H2

Example H3

Sync data between My Hours and Kafka

Get your data syncing in minutes

Try Airbyte free

TL;DR

set up My Hours as a source connector (using Auth, or usually an API key)
set up Kafka 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 My Hours