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Current Version: v.1.7.0

Version Release Schedule

  • v0.1.0 (Pilot) August 3, 2024

  • v1.0.0 August 17, 2024

  • v1.1.0 August 31, 2024

  • v1.2.0 September 14, 2024

  • v1.3.0 September 28, 2024

  • v1.4.0 October 12, 2024

  • v1.5.0 October 26, 2024

  • v1.6.0 November 9, 2024

  • v1.7.0 November 23, 2024

Supported Connectors:

  • MySQL (since v0.1.0 Pilot)

  • IBM DB2 (since v0.1.0 Pilot)

  • Oracle (since v0.1.0 Pilot)

  • Postgres (since v0.1.0 Pilot)

  • Snowflake Data Warehouse (since v1.0.0)

  • Salesforce (since v1.0.0)

  • MongoDB (since v1.0.0)

  • Azure Cosmos MongoDB (since v1.0.0)

  • Azure Cosmos PostgreSQL (since v1.0.0)

  • Azure PostgreSQL (since v1.0.0)

  • Azure MySQL (since v1.0.0)

  • Azure SqlServer (since v1.0.0)

  • Google CLoud MongoDB Atlas (since v1.0.0)

  • AWS Aurora MySQL (since v1.1.0)

  • AWS Aurora PostgreSQL (since v1.1.0)

  • AWS RDS PostgreSQL (since v1.1.0)

  • AWS RDS MySQL (since v1.1.0)

  • AWS RDS SqlServer (since v1.1.0)

  • AWS RDS Oracle (since v1.1.0)

  • AWS RDS MariaDB (since v1.1.0)

  • Clickhouse DB (since v1.1.0)

  • ElasticSearch (since v1.1.0)

  • Fauna DB (since v1.1.0)

  • Cockroach DB (since v1.1.0)

  • Amazon S3 (since v1.2.0)

  • Amazon S3 Csv (since v1.2.0)

  • Amazon S3 Excel (since v1.2.0)

  • Azure Data Lake (since v1.2.0)

  • Azure Data Lake Csv (since v1.2.0)

  • Azure Data Lake Excel (since v1.2.0)

  • FTP (since v1.2.0)

  • FTP Csv (since v1.2.0)

  • FTP Excel (since v1.2.0)

  • Google Sheets (since v1.2.0)

  • Google Firebase (since v1.3.0)

  • Google Firestore (since v1.3.0)

  • Heroku Postgres (since v1.3.0)

  • Google Cloud Platform SqlServer (since v1.3.0)

  • Google Cloud Platform MySQL (since v1.3.0)

  • Google Cloud Platform PostgreSql (since v1.3.0)

  • CSV File for Agent (since v1.4.0)

  • Excel File for Agent (since v1.4.0)

  • Rest Api Connector (since v1.5.0)

Installation and Minimum Hardware Requirements

Note: We advise you to use one secure agent per machine as the way it is registered in Eazydi is as follows:

  • system: Operating system (Windows or Linux)

  • name: name of the machine on the network (unique)

  • user: The user who registered the Agent to EazyDI

Minimum Hardware Requirements

...

Follow these steps to install the Secure Agent on your system.

Prerequisites

(Python 3.9)

(Install JDK)

For Windows:

  1. Download JDK:

    • Download the latest JDK from the official Oracle or OpenJDK Website

  2. Install JDK:

    • Run the installer and follow the on-screen instructions to install the JDK.

  3. Set JAVA_HOME Environment Variable:

    • Open the Start menu and search for "Environment Variables".

    • Click on "Edit the system environment variables".

    • In the System Properties window, click on the "Environment Variables" button.

    • Under "System variables", click "New" and set the variable name to JAVA_HOME and the variable value to the JDK installation path (e.g., C:\Program Files\Java\jdk-11).

    • Find the Path variable in the "System variables" section, select it, and click "Edit".

    • Click "New" and add %JAVA_HOME%\bin to the list.

    • Click "OK" to close all dialogs.

...

  • base_socket_url

    • Description: The URL used by the Secure Agent to establish a connection with the EazyDi platform.

    • Purpose: This is a crucial setting that should not be altered. It specifies the endpoint for all communication between the agent and the cloud service.

  • username

    • Description: The username associated with your EazyDi account.

    • Purpose: This parameter is used for authenticating the Agent with the EazyDi platform. It ensures that the agent operates under the correct user context.

    • Example: username=myusername@eazydi.com

  • token

    • Description: The authentication token provided by EazyDi.

    • Purpose: This token is used in conjunction with the username to authenticate the Secure Agent. It is essential for secure and authorized access to the platform.

    • Example: token=320af1e1-5786-4779-bf20-1ecadb92c900

  • spark_memory

    • Description: The amount of memory allocated to Apache Spark.

    • Purpose: This setting determines how much memory is available for Spark jobs executed by the Agent. Adequate memory allocation is crucial for performance, especially when handling large datasets. see https://spark.apache.org/docs/latest/configuration.html

    • Example: spark_memory=6g

  • spark_master

  • spark_cores

    • Description: The number of CPU cores allocated to Spark.

    • Purpose: This parameter sets the number of CPU cores available for Spark processing tasks. More cores can improve performance by allowing more parallel processing. see https://spark.apache.org/docs/latest/configuration.html

    • Example: spark_cores=2

For Windows

...

  • Once Job is complete it will reflect in Eazydi’s Monitor → Archived Listings

image-20240731-111712.png

Pre and Post Environment Script execution (since v1.6.0)

See https://eazydi-doc.atlassian.net/wiki/spaces/EazyDIDOC/pages/132907059/Pipeline+Pre+and+Post+Script+Executions#Agent-Scripts-Execution