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Try them now for free →Migrating data from Presto to Databricks using CData SSIS Components.
Easily push Presto data to Databricks using the CData SSIS Tasks for Presto and Databricks.
Databricks is a unified data analytics platform that allows organizations to easily process, analyze, and visualize large amounts of data. It combines data engineering, data science, and machine learning capabilities in a single platform, making it easier for teams to collaborate and derive insights from their data.
The CData SSIS Components enhance SQL Server Integration Services by enabling users to easily import and export data from various sources and destinations.
In this article, we explore the data type mapping considerations when exporting to Databricks and walk through how to migrate Presto data to Databricks using the CData SSIS Components for Presto and Databricks.
Data Type Mapping
Databricks Schema | CData Schema |
---|---|
int, integer, int32 |
int |
smallint, short, int16 |
smallint |
double, float, real |
float |
date |
date |
datetime, timestamp |
datetime |
time, timespan |
time |
string, varchar |
If length > 4000: nvarchar(max), Otherwise: nvarchar(length) |
long, int64, bigint |
bigint |
boolean, bool |
tinyint |
decimal, numeric |
decimal |
uuid |
nvarchar(length) |
binary, varbinary, longvarbinary |
binary(1000) or varbinary(max) after SQL Server 2000 |
Special Considerations
- String/VARCHAR: String columns from Databricks can map to different data types depending on the length of the column. If the column length exceeds 4000, then the column is mapped to nvarchar (max). Otherwise, the column is mapped to nvarchar (length).
- DECIMAL Databricks supports DECIMAL types up to 38 digits of precision, but any source column beyond that can cause load errors.
About Presto Data Integration
Accessing and integrating live data from Trino and Presto SQL engines has never been easier with CData. Customers rely on CData connectivity to:
- Access data from Trino v345 and above (formerly PrestoSQL) and Presto v0.242 and above (formerly PrestoDB)
- Read and write access all of the data underlying your Trino or Presto instances
- Optimized query generation for maximum throughput.
Presto and Trino allow users to access a variety of underlying data sources through a single endpoint. When paired with CData connectivity, users get pure, SQL-92 access to their instances, allowing them to integrate business data with a data warehouse or easily access live data directly from their preferred tools, like Power BI and Tableau.
In many cases, CData's live connectivity surpasses the native import functionality available in tools. One customer was unable to effectively use Power BI due to the size of the datasets needed for reporting. When the company implemented the CData Power BI Connector for Presto they were able to generate reports in real-time using the DirectQuery connection mode.
Getting Started
Prerequisites
- Visual Studio 2022
- SQL Server Integration Services Projects extension for Visual Studio 2022
- CData SSIS Components for Databricks
- CData SSIS Components for Presto
Create the project and add components
-
Open Visual Studio and create a new Integration Services Project.
- Add a new Data Flow Task to the Control Flow screen and open the Data Flow Task.
-
Add a CData Presto Source control and a CData Databricks Destination control to the data flow task.
Configure the Presto source
Follow the steps below to specify properties required to connect to Presto.
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Double-click the CData Presto Source to open the source component editor and add a new connection.
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In the CData Presto Connection Manager, configure the connection properties, then test and save the connection.
Set the Server and Port connection properties to connect, in addition to any authentication properties that may be required.
To enable TLS/SSL, set UseSSL to true.
Authenticating with LDAP
In order to authenticate with LDAP, set the following connection properties:
- AuthScheme: Set this to LDAP.
- User: The username being authenticated with in LDAP.
- Password: The password associated with the User you are authenticating against LDAP with.
Authenticating with Kerberos
In order to authenticate with KERBEROS, set the following connection properties:
- AuthScheme: Set this to KERBEROS.
- KerberosKDC: The Kerberos Key Distribution Center (KDC) service used to authenticate the user.
- KerberosRealm: The Kerberos Realm used to authenticate the user with.
- KerberosSPN: The Service Principal Name for the Kerberos Domain Controller.
- KerberosKeytabFile: The Keytab file containing your pairs of Kerberos principals and encrypted keys.
- User: The user who is authenticating to Kerberos.
- Password: The password used to authenticate to Kerberos.
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After saving the connection, select "Table or view" and select the table or view to export into Databricks, then close the CData Presto Source Editor.
Configure the Databricks destination
With the Presto Source configured, we can configure the Databricks connection and map the columns.
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Double-click the CData Databricks Destination to open the destination component editor and add a new connection.
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In the CData Databricks Connection Manager, configure the connection properties, then test and save the connection. To connect to a Databricks cluster, set the properties as described below.
Note: The needed values can be found in your Databricks instance by navigating to Clusters, selecting the desired cluster, and selecting the JDBC/ODBC tab under Advanced Options.
- Server: Set to the Server Hostname of your Databricks cluster.
- HTTPPath: Set to the HTTP Path of your Databricks cluster.
- Token: Set to your personal access token (this value can be obtained by navigating to the User Settings page of your Databricks instance and selecting the Access Tokens tab).
Other helpful connection properties
- QueryPassthrough: When this is set to True, queries are passed through directly to Databricks.
- ConvertDateTimetoGMT: When this is set to True, the components will convert date-time values to GMT, instead of the local time of the machine.
- UseUploadApi: Setting this property to true will improve performance if there is a large amount of data in a Bulk INSERT operation.
- UseCloudFetch: This option specifies whether to use CloudFetch to improve query efficiency when the table contains over one million entries.
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After saving the connection, select a table in the Use a Table menu and in the Action menu, select Insert.
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On the Column Mappings tab, configure the mappings from the input columns to the destination columns.
Run the project
You can now run the project. After the SSIS Task has finished executing, data from your SQL table will be exported to the chosen table.