Teradata Migration: AWS Redshift, GCP Big Query, Oracle ADW, Azure Synapse Cloud

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It's no longer a secret that several customers have been moving their teradata warehouse to GCP big query, Oracle ADW, AWS Redshift, SnowFlake, and Azure SQL. Vast Edge has expertise in migrating Teradata to these cloud based warehouses to save upto 50% in costs, increase speeds, leverage modern analytics and next generation data warehouse features. Since 2004, Vast Edge has been assisting businesses to optimize their processes by leveraging cutting edge technologies. Vast Edge has certified experts across Oracle, Google, Azure, and AWS clouds.

Migration Strategy By Cloud Platform: GCP Big Query AWS Redshift Azure SQL OCI ADW

Teradata Migration to Oracle Autonomous Data Warehouse (ADW)

Teradata Migration to ADW offered by Vast Edge provides an optimized and pre-configured database capable of Self-Patching, Self-Tuning, and automated upgrades. Our optimized Teradata Migration to Oracle ADW solution offers:

With Teradata Migration to ADW, Vast Edge helps businesses to reduce cost up to 50% while ensuring higher database availability. Vast Edge deploys an expeditious and scalable data-loading from Oracle Object Store, AWS S3, or on-premise that avails raw partitioning by incrementing its performance. Teradata Migration to OAD warehouse, offered by Vast Edge, manages data workloads with 100% compatibility ascertaining customers to extract data insights and make critical decisions in authentic-time.

Why Migrate to Oracle Autonomous Data Warehouse

1
Oracle ADW allows pay-as-you-use model similar to a metered connection or monthly subscription model.
2
The service comes with an integrated Oracle Apex environment, allowing you to move development activities to the cloud faster.
3
Oracle Database Multi-tenant makes it easy to consolidate a number of databases quickly and manage them as a cloud service.
4
Oracle Database introduces several capabilities that significantly reduce the cost and time required to migrate non-Oracle databases to the Oracle platform.
5
Teradata migration to ADW provisions high availability configurations to elevate service levels for maintenance or unexpected failure scenarios.

Various phases of Migration Strategy

Discovery and identification of existing database

1
Version of the database
2
Number of databases to be migrated
3
Size of the database
4
Stored Procedures and triggers need to be migrated
5
Downtime/maintenance window
6
Application interfaces

Implementation

1
Connect
Connect to the source database.
2
Capture
This online migration phase leverages the network that you provided to the third-party database while the output is displayed in the Captured Database.
3
Convert
In the next step the captured model of the database is converted to an Oracle-specific model.
4
Generate
In this phase, the generate SQL scripts is used for forming the new ODA schema(s) and to run these scripts.
5
Data Move
In the last step, copying the data from the third-party database to the new tables in the ODA Warehouse is carried out.

Teradata Migration to Azure SQL Warehouse

Vast Edge offers SQL server management studio for migrating Teradata to Azure cloud to reduce the time consumption during large data migration. Our cloud migration specialists employ a controlled migration environment to truncate non-Azure assets for higher availability and on-demand migrations by spinning up or scheduling Azure resources as required.

1
Vast Edge executes integrated approach backed by ETL implements like SSIS or Azure Data Factory along with data management gateway for better connectivity and reliable migration.
2
Leveraging Teradata computing abilities, Vast Edge integrates your Azure ecosystem with supplemental data processing capabilities leveraging artificial intelligence and machine learning.
3
Teradata along with Azure SQL warehouse deployment by Vast Edge combines the performance and scalability of both of the platforms through a massively parallel processing data warehouse while ensuring the ease-of-use of SaaS features.
4
The Teradata solutions deployed by Vast Edge in Azure warehouse enables an on-demand query optimizer to ingest, prepare, manage, and accommodate data top-notch business intelligence and machine learning needs. Besides, our migration strategy harnesses the data conversion feature that leads to an error-free extraction of data through metadata compatibility.
5
For secure Teradata migration, Vast Edge deploys hash and round-robin distribution in rows and columns to identify recently used data and further authenticating them.
6
Vast Edge ensures an astute, affordable, worry-free Teradata migration for driving the highest business performance.

Various phases of Teradata to Azure SQL Data Warehouse migration:

Fact-Finding

This is the data discovery step which determines inputs and outputs for migration.

Proof of Concept

A workload is identified to validate the outputs required and run the following phases as a POC.

Data Layering

This phase is about mapping the data you have in Teradata to the data layout you will create in Azure SQL Data Warehouse.

Data Modeling

This phase concentrates on how to tune the Azure SQL Data Warehouse.

Identify Migration Paths

This phase is used to identify the migration paths.

Execution

In this step, actual migration is done.

Azure SQL DW Migration Steps

1
Preparation Of Data Migration
You can provision a Teradata database in Azure, before migrating the data from an on-premise database. This can speed up the migration efforts.
2
Migration Of Business Logic
To migrate business logic, either lift and shift approach can be used or business logic can be rewritten in Azure Data Warehouse.
3
Source-controlled pipelineData Migration
There are multiple ways to do data migration and choice will depend on the connectivity you have from your datacenter to Azure. There are 2 ways to do that:

Source-controlled pipeline: Data export, transfer and import steps run from the source system. This approach uses existing computer and storage resources at the source system.
Azure-controlled Pipeline: Data export, transfer and import steps run from the Azure. Migration logic is run in virtual machines running on Azure, with the virtual machines being allocated and deallocated on demand.

Teradata Migration to AWS Redshift

For migrating Teradata to Amazon Redshift, Vast Edge leverages the AWS Schema Conversion Tool (AWS SCT) to accumulate information from source database schema and convert them to an AWS database. Vast Edge offers a top-notch data warehouse with industry-grade scalability to harness the cloud technology with business perspicacity implements.

The key differentiator of Vast Edge's offerings for Teradata Migration to AWS Redshift are:

PostgreSQL Based Migration

* Recursive SQL * Table sampling * RA3 instances * Partial aggregates with filter clause * Hypothetical aggregates for higher compatibility

Amazon Redshift Deployment

* JDBC drivers * Teradata insights for data transformation * Minimum manual coding * End-to-end analytics * Flexibility to pay discretely

Preparation Of Data Migration

* Easy workflow * Procure to pay-per-session pricing * Dynamic reports and visualizations * Customizable dashboards * Maximized performance

Vast Edge utilizes sophisticated algorithms to prognosticate and relegate incoming queries predicated on their run time and resource requisites to dynamically manage performance while prioritizing business-critical workloads while migrating Teradata to Amazon AWS.

Teradata Migration to Google Cloud Platform (GCP) Big Query

Teradata migration to GCP BigQuery by Vast Edge enables extremely fast analytics on a petabyte-scale with unique architecture and state-of-the-art capabilities. Vast Edge eliminates the need to forecast and provision storage as in our solutions the resources are allocated dynamically

Why migrate to Google Cloud Big Query?

1
Both Big Query and Teradata Database conform to the ANSI/ISO SQL:2011
2
Provides pay-as-you-go model.
3
Big Query eliminates the need to forecast and provision storage and compute resources in advance.
4
Big Query charges separately for data storage and query processing, enabling an optimal cost model.
5
Big Query provides the ability to connect to federated (external) data sources such as Google Cloud Big Table, Google Cloud Storage (GCS) and Google Drive.
6
Direct Integration with the most popular BI tools.
7
BTEC and Fast Load etc. allow for high-performance data ingestion and processing.
8
Very capable enterprise data warehouse platform leveraging the MPP architecture.

Steps to Migrate to GCP Big Query

Discovery Of Database Schema

* Version of the database.
* Stored Procedures and triggers migration.
* Pipeline dependencies and scheduling requirements.
* Monitoring, auditing, and logging requirements.
* Reporting requirements including KPIs and SLAs.
* Downtime/maintenance window.

Identification Of Database Schema

* Identification of use cases that needs to be migrated to Big Query.
* Transfer the group of tables for each use case to BigQuery without any changes.
* Configuring downstream systems for testing to read data from Big * Query.
* Configuring upstream systems for testing to write data to Big Query.

Implementation Challenges

* Big Query does not support stored procedures. Some of your queries might need to be refactored during migration.
* Differences between Teradata SQL and the Big Query standard SQL.
* Big Query supports a more concise set of data types than Teradata, with groups of Teradata types mapping into a single standard SQL data type.
* Big Query is an enterprise data warehouse that focuses on Online Analytical Processing (OLAP). This is not a correct approach to treat Big Query like an OLTP system which is done in Teradata.

Modifications of stored Procedures

1
Replace triggers that are used to run periodic queries with scheduled queries.
2
Replace stored procedures that control the complex execution of queries and their interdependencies with workflows defined in Cloud Composer
3
Refactor stored procedures that are used as an API into your data warehouse with parameterized queries and using the Big Query API

Query Translation

1
Teradata SQL is translated to basic SQL so that it can be used in Big Query.
2
Use script to search and replace SQL elements. e.g GT is replacing with using sed command.
3
Non-standard functions need to be converted into equivalent standard SQL.

Teradata to Snowflake Migration

A snowflake is a native-cloud software whereas Teradata comes with hardware and software which is mounted on-premises. In the case of Snowflake, the data, the software and the SQL client which is used to access the warehouse, all come in native cloud state. Snowflake just takes a few clicks to create a new data warehouse and to add-on instances.

Reduced Complexities

Unlike Teradata, Snowflake uses cloud (AWS / Azure / GCP / Oracle) hardware and its own extended layer to manage the resources and users.

Unlimited Capacity

Since Snowflake is fully dedicated to the cloud, there is no need to purchase additional hardware for extending storage or compute.

Virtual Warehousing

Hardware apps has to be shared between the ETL and the Reporting teams in the case of Teradata. However, Snowflake uses virtual warehouses so that each team can create its own warehouse in real-time without the need for copying the data.