Data integration from a database like Oracle to an analytics environment like Azure Synapse Analytics is one of the most crucial steps for any business. It can help you analyze the data more efficiently and produce insights, resulting in effective decision-making.
Azure Synapse Analytics’s ADX dashboard features allow you to explore your data from an end-to-end analytics workflow. Its in-built user-defined functions feature enables you to reuse functions across many data flows.
This article discusses two of the most widely used methods of moving data from Oracle to Azure Synapse Analytics.
Why Integrate Oracle to Azure Synapse Analytics?
There are many reasons to move data from an Oracle database to an analytics platform like Azure Synapse Analytics. Here are some of the most common benefits:
- Integrating Oracle to Azure Synapse Analytics can offer a better analytical environment where you can perform multiple machine learning tasks.
- Azure Synapse provides advanced security features, such as dynamic data-masking abilities and row and column-level security.
- Oracle to Azure Synapse Analytics allows you to query relational and non-relational data using the programming language of your preference.
An Overview of Oracle Database
Oracle Database is a prominent relational database management system (RDBMS) that stores organizes, and retrieves data according to user requirements. Storing data in the Oracle Database allows users to quickly create scalable and high-performance applications using SQL or some other procedural language.
With features like Oracle Active Data Guard and Oracle Real Application Clusters, you can efficiently scale and consolidate customer databases, maximizing their availability. It provides a single-pane-of-glass management dashboard to help you increase business performance while constantly managing processes.
An Overview of Azure Synapse Analytics
Azure Synapse Analytics is Microsoft’s analytics service that generates insights from any organization’s on-premise data. It combines the capabilities of SQL, Azure Spark, and Azure Data Explorer to manage big data and perform time series analysis. Azure Synapse Analytics provides advanced privacy features, like always-on encryption and automated threat detection.
Its pay-as-you-go feature allows you to manage costs by using the services you need while maintaining the cost-effectiveness of the analytics procedure. As Azure Synapse Analytics runs on the Microsoft ecosystem, it is easy to connect it with other software on the Azure marketplace. Refer to Azure Synapse Analytics benefits to know more about it.
Methods to Connect Oracle to Azure
If you wonder, “How do I migrate from Oracle to Azure?” This section discusses the most prominently used methods.
Method 1: Copy Data from Oracle to Azure Synapse Using Hevo Data
Hevo Data is a no-code, real-time ELT data pipeline platform that cost-effectively automates your data pipeline according to your requirements. With its easy-to-use user interface, you can integrate data from 150+ source connectors to a destination without prior technical expertise.
Here are some of the key features of using Hevo:
- Data Transformation: Hevo Data enables you to clean and prepare the data to be loaded into the destination using Python-based and drag-and-drop methods.
- Automated Schema Mapping: Hevo automatically manages the schema by detecting the format of incoming data and replicating it to the destination schema. It lets you choose between Full & Incremental Mappings according to your data replication requirements.
- Incremental Data Load: Hevo ensures efficient bandwidth utilization on both the source and the destination by allowing modified data transfer in real-time.
Hevo Data can enable you to perform Oracle to Azure Synapse replication in real-time.
Configure Oracle as Source
This section highlights the steps to configure the Oracle database as a source in Hevo. Before getting started, ensure that you satisfy the following prerequisites.
Prerequisites:
- You must have the Oracle database version 11 or above. To retrieve the version of the Oracle database, follow this command:
SELECT BANNER_FULL FROM V$VERSION WHERE BANNER_FULL LIKE ‘Oracle Database%’;
After satisfying the prerequisite conditions, you must follow the steps below to configure the Oracle database as a source of your data pipeline.
- Select PIPELINES from the Navigation Bar.
- On the Pipelines List View, click + CREATE.
- Select Oracle on the Select Source Type page.
- Specify all the necessary fields on the Configure your Oracle Source Page:
Oracle to Azure Synapse: Configure your Oracle Source
- Finally, click on TEST CONNECTION and select TEST & CONTINUE. After following these steps, you can configure object and query mode settings according to the chosen data ingestion mode.
To dive deeper into the steps involved, refer to Hevo Oracle Source Documentation.
Configure Azure Synapse as Destination
This section highlights the steps to set Azure Synapse as a destination in your Hevo data pipeline. Here’s an image showing the procedures for configuring Azure Synapse as a destination in Hevo.
Oracle to Azure Synapse: Configure Azure Synapse Analytics as Destination
Before getting into the steps involved, ensure that you satisfy the following prerequisites.
Prerequisites:
After satisfying all the prerequisite conditions, you can follow the steps below to configure Azure Synapse as a destination in Hevo.
- Select DESTINATIONS from the Navigation Bar and click on + CREATE in the Destinations List View.
- Select Azure Synapse Analytics on the Add Destination page.
- Specify the mandatory details on the Configure your Azure Synapse Analytics Destination page.
Oracle to Azure Synapse: Configure your Azure Synapse Analytics Destination
- Finally, click on TEST CONNECTION and select SAVE & CONTINUE.
Refer to the Azure Synapse Analytics Hevo Documentation to learn more about the steps involved in configuring Azure Synapse Analytics as a destination in Hevo.
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Method 2: Integrating Oracle to Azure Synapse Using CSV Data File Transfer
This method will highlight how to connect Oracle Database to Azure Synapse using CSV data file transfer. Follow the steps given below:
Step 1: Export CSV Data from Oracle Database
To export data from the Oracle database to your local system, you can use cx_Oracle, a Python module that interacts with Oracle. Run the following command by entering your specific information on the database and columns placeholders:
import os
import csv
import sys
import cx_Oracle
db = cx_Oracle.connect('user/pass@host:port/service_name')
SQL = "select column1 , column2 , .... , column from table"
print(SQL)
cursor = db.cursor()
f = open("C:\myfile.csv", "w")
writer = csv.writer(f, lineterminator="\n", quoting=csv.QUOTE_NONNUMERIC)
r = cursor.execute(SQL)
#this takes the column names
col_names = [row[0] for row in cursor.description]
writer.writerow(col_names)
for row in cursor:
writer.writerow(row)
f.close()
Step 2: Move Data to Azure Synapse Analytics
To move the CSV file data from your local machine to Azure Synapse Analytics, run the code snippet in an SQL Client tool such as Azure Synapse Studio by removing the placeholder values.
COPY INTO [ schema. ] table_name
[ (Column_list) ]
FROM '<external_location>' [ , ...n ]
WITH
(
[ FILE_TYPE = { 'CSV' | 'PARQUET' | 'ORC' } ]
[ , FILE_FORMAT = EXTERNAL FILE FORMAT OBJECT ]
[ , CREDENTIAL = (AZURE CREDENTIAL) ]
[ , ERRORFILE = ' [ http(s)://storageaccount/container ] /errorfile_directory [ / ] ] '
[ , ERRORFILE_CREDENTIAL = (AZURE CREDENTIAL) ]
[ , MAXERRORS = max_errors ]
[ , COMPRESSION = { 'Gzip' | 'DefaultCodec' | 'Snappy' } ]
[ , FIELDQUOTE = 'string_delimiter' ]
[ , FIELDTERMINATOR = 'field_terminator' ]
[ , ROWTERMINATOR = 'row_terminator' ]
[ , FIRSTROW = first_row ]
[ , DATEFORMAT = 'date_format' ]
[ , ENCODING = { 'UTF8' | 'UTF16' } ]
[ , IDENTITY_INSERT = { 'ON' | 'OFF' } ]
[ , AUTO_CREATE_TABLE = { 'ON' | 'OFF' } ]
)
Limitations of Using CSV Data File Transfer Method
Although the second method is efficient in moving data from Oracle to Azure Synapse Analytics, there are some limitations associated with this method.
- Error Production: For large datasets involving complex multiple data structures, the process of moving data using the COPY INTO command can get complicated, requiring additional monitoring while running the code.
- Limitations on File Formats: The COPY INTO command supports limited formats of data files, including CSV, Parquet, and ORC.
- Lack of Automation: This method requires you to move data using code snippets manually and lacks the real-time integration factor. Any changes on the source do not reflect on the destination. To achieve this, you must continually perform the steps at regular intervals.
Use Cases of Integrating Oracle to Azure Synapse
- Integrating Oracle to Azure Synapse Analytics can enable you to use machine learning to uncover insights from your data.
- This migration can help you save time, as Azure Synapse provides a unified platform for developing end-to-end analytics applications.
- Azure Synapse Analytics provides workload isolation, management, and limitless concurrency that can enable you to optimize the performance of your queries.
Conclusion
This article mentions two of the most widely used Oracle to Azure Synapse Analytics data integration methods. Both methods efficiently load data from Oracle to Azure Synapse. However, the second method has limitations. To overcome these limitations, you can use Hevo to integrate data.
Hevo provides 150+ data source connectors from which you can quickly extract data and load it into the destination of your choice.
Want to take Hevo for a spin? Sign Up for a 14-day free trial and experience the feature-rich Hevo suite firsthand. Also checkout our unbeatable pricing to choose the best plan for your organization.
Share your experience of Oracle to Azure Synapse integration in the comments section below!
Frequently Asked Questions (FAQs)
Q. What are the key features of using Azure Synapse Analytics?
- There are various advantages of using Azure Synapse Analytics as a data pipeline destination. Here are some of the key benefits:
- Scalability: Azure Synapse Analytics allows you to scale compute resources without affecting storage while avoiding over-provisioning.
- Analytics Capabilities: Azure Synapse Analytics enables you to perform advanced analytics using machine learning models and embedded AI services.
- Unified Platform: It streamlines your workflow and reduces complexity by providing a single interface for data integration, warehousing, and analytics.
- Cost Effectiveness: You choose the services according to your requirements and pay for the services you use. This advantage can enable you to cut extra costs associated with costly monthly subscriptions.
Dimple is an experienced Customer Experience Engineer with four years of industry proficiency, including the last two years at Hevo, where she has significantly refined customer experiences within the innovative data integration platform. She is skilled in computer science, databases, Java, and management. Dimple holds a B.Tech in Computer Science and excels in delivering exceptional consulting services. Her contributions have greatly enhanced customer satisfaction and operational efficiency.
