Integration

Lyftrondata Data Mirror is a feature within Lyftrondata platform that allows for the replication and synchronization of data from various sources to a centralized data warehouse or data lake in real-time or near real-time. This helps in creating an up-to-date, unified view of the data across the organization.

Integration is Divided into 5 Simple Steps:

1. Prep

2. Select Source

3. Select Target

4. Configuration

5. Confirm

Warehouse

Warehouse:

Warehouse (also known as a virtual warehouse) is a key component that plays a central role in processing Integrations. It is a cluster of computing resources (e.g., CPU, memory) that users can provision to perform data processing tasks.

Components of Warehouse:

Key	Value
Name	Warehouse Name
CPU	No of CPU's
Memory	Amount of memory
IP address	Public IP Address

Whitelist Warehouse IP:

To ensure proper functionality, please whitelist the IP address in your environment. This will allow necessary access and prevent any connectivity issues.

Whitelist

Select Source: Lyftrondata integrates with over 300 data sources. You simply need to select the source from which to load your data into the target.

Lyftrondata Connectors

Freshsales Connection Name

Key	Description
Connection Name	You need to write a meaningful connection name.
Description	Short description of the connection name.
Tag	Tags for a connection are keywords or labels assigned to a data connection to categorize and organize it.

Configuration

You need to complete the prerequisites for the API in order to obtain the credentials. Some APIs require payment, while others are free to use. I have used Freshsales API as an example.

Key	Value
Personal Token	Your Freshsales API Personal Token.
Base URL	Your Freshsales API BASE URL.
Hostname	Your Freshsales Hostname.

Select Target: Lyftrondata's target refers to the destination where data is transferred, transformed, or loaded during data integration processes. It could include databases, data warehouses, data lakes, cloud storage services, or other platforms where the processed data is ultimately stored or used for further analysis and reporting.

Target Connectors

Target Connection Name

Key	Description
Connection Name	You need to write a meaningful connection name.
Description	Short description of the connection name.
Tag	Tags for a connection are keywords or labels assigned to a data connection to categorize and organize it.

Configuration Screen Target

Basic

Key	Value	Field
URL	Your snowflake account URL.	Required
Username	Enter your snowflake Username.	Required
Password	Enter your snowflake Password.	Required
Schema	Enter your snowflake Schema.	Required
Role	Enter your snowflake Role.	Required
Warehouse	Enter your snowflake Warehouse.	Required
Database	Enter your snowflake Database.	Required

Target Snowflake Connection Video:

Snowflake

After setting up the target, the integration configuration process begins, defining data flow through mappings, transformations, and schedules for efficient, accurate processing. Batches manage data transfer size and frequency to optimize performance, while logging tracks each step for troubleshooting and monitoring. Webhooks trigger actions on event-based notifications, enhancing automation in real-time data workflows.

Integration

Process Configuration

Config Parameters	Description
Batch Size	Batch size is the number of data records processed together in a single operation, optimizing performance and resource use.
Select Memory Size	Refers to choosing the amount of memory allocated for a specific task.
Regex	A sequence of characters that defines a search pattern for matching, replacing, and extracting text.
Die on Error	Immediately stop a program or process when an error occurs, preventing any further execution.
Process Method	Process method parquet or Avro" refers to the choice between using the Parquet or Avro file formats during data processing.
Pipeline Parallelism	Pipeline parallelism means how many pipelines can run in parallel.
Enable Multithreading	Execute multiple threads concurrently, improving performance by utilizing multiple CPU cores effectively
Pipeline Per Dag Limit	Pipeline per DAG means how many pipelines/tables you can select in a single integration.

You need to select the target schema in the load configuration.

Load Configuration Schema

You can schedule the integration based on your specific time.

Schedule Integration

If you want to receive notifications through email or a Slack channel, you can configure that. You will get notifications for any event, whether it passes or fails.

Notification

You have the option to select your preferred logging service for tracking and monitoring your data integration processes. Choose between Lyftrondata or CloudWatch to ensure you receive timely and detailed logs of all activities.

Logging

You can also set up Web Hook Calls to receive real-time notifications and updates. This allows you to instantly react to events and integrate with other systems seamlessly.

Webhooks Call

Data Mirror Integration: