How to load data from Tempo to AWS Datalake

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

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Set up a Tempo connector in Airbyte

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

Set up AWS Datalake for your extracted Tempo data

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

Configure the Tempo 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 Tempo 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 Tempo

Tempo is a global software-as-a-service company (SaaS) focused on providing companies with productivity and time management tools to drive more efficient and successful business. Products include resource planning, budget management, and world-class time tracking solutions for Jira (Tempo has claimed ownership to the #1 Jira time tracking app since 2010). Tempo drives business success by providing software that affords insights into teams’ productivity capabilities.

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 Tempo with AWS Datalake in minutes

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Prerequisites

A Tempo 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 Tempo and AWS Datalake, for seamless data migration.

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

Step 1: Set up Tempo as a source connector

1. Open the Airbyte platform and navigate to the "Sources" tab on the left-hand side of the screen.

2. Click on the "Add Source" button in the top right corner of the screen.

3. In the search bar, type "Tempo" and select the Tempo source connector from the list of available connectors.

4. Enter a name for the connector and click on the "Next" button.

5. Enter your Tempo API credentials, including the API key and the base URL for your Tempo instance.

6. Test the connection to ensure that the credentials are correct and the connection is successful.

7. Select the data you want to replicate from Tempo, including the projects, tasks, and time entries.

8. Choose the replication frequency and the destination where you want to store the replicated data.

9. Click on the "Create Source" button to save the connector and start the replication process.

10. Monitor the replication process and troubleshoot any errors that may occur using the Airbyte platform's built-in tools and resources.

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 Tempo data to AWS Datalake

Once you've successfully connected Tempo 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 Tempo 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 Tempo 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 Tempo 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 Tempo data.

Use Cases to transfer your Tempo data to AWS Datalake

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

What data can you extract from Tempo?

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

1. Time tracking data: This includes data related to time entries, such as start and end times, duration, and comments.

2. Resource management data: This includes data related to resources, such as employee information, team information, and workload.

3. Project management data: This includes data related to projects, such as project information, project status, and project timelines.

4. Billing and invoicing data: This includes data related to billing and invoicing, such as billing rates, invoices, and payment information.

5. Reporting data: This includes data related to reporting, such as timesheet reports, project reports, and resource reports.

6. Custom fields data: This includes data related to custom fields, such as custom fields for time entries, resources, and projects.

Overall, Tempo's API provides a comprehensive set of data that can be used to manage time, resources, and projects more effectively.

How do I transfer data from Tempo?

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 Tempo 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 Tempo 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 Tempo to AWS Datalake

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

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Deploy Airbyte Open Source

Step 2: Setup AWS Datalake

Step 3: Setup a connection Use Cases Wrapping Up

Example H2

Example H3

Sync data between Tempo and AWS Datalake

Get your data syncing in minutes

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TL;DR

set up Tempo 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 Tempo

What is AWS Datalake

Integrate Tempo with AWS Datalake in minutes

Try for free now

Prerequisites

A Tempo 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.