Back FAQ
Get answers to most commonly asked questions around converting Teradata to cloud / big data
The Impetus Workload Transformation Solution is an accelerated service where the Impetus team actively collaborates with the customer. We offer workload transformation as a service for the following reasons:
The automated conversion percentage is dependent on every customer’s unique environment and requirements
Our underlying configurable, self-learning, and extensible grammar engine needs time to learn these new patterns
Workload assessment is an extremely powerful tool that can be created in seconds and empowered by one-click execution.
More than 90%. Additionally, we provide assisted curation for the rest 10% of the workloads.
Yes, system-generated recommendations are provided, which help in creating a prioritized blueprint of use cases based on critical factors such as cost, risk, business objectives, and more.
Impetus recommends launching the transformation with a risk-free, comprehensive 4-8week assessment, which includes a pilot (to prove the automagical conversion aspect, build confidence, and demonstrate ROI) and an end-to-end use case. The POC takes 6-9 weeks where we identify how much time it would take to transform all the workloads.
We’ve observed that the Impetus Teradata Workload Transformation Solution can save up to 70-80% of time compared to manual transformation. This is because we automate the entire process, end-to-end, from assessment and transformation to validation and execution.
This tool is completely automated and de-risks all factors that may potentially jeopardize the modernization initiative.
The overall pricing depends on the number of unique queries running in Teradata. Impetus can establish the cost after the assessment phase.
The Impetus Workload Transformation Solution supports AWS, Azure, and generic IaaS cloud providers that have Hive 1.3+ and/or Spark 1.6+ in the form of IaaS or PaaS, such as EMR and Databricks.
The Impetus Workload Transformation Solution supports all Hadoop distros, including HDP, CDH, MapR, IBM Hadoop, Oracle Hadoop Appliance, AWS EMR, and Azure HDInsight that support Hive 1.3+ and/or Spark 1.6+.
Yes, the tool ensures that end-to-end SLAs are met as required for the production environment.
Yes, it does support sequence control statements such as GOTO, LABEL, IF…THEN, and more.
Yes, it does support the conversion of views as views and views as tables.
Yes, it offers numerous recommendations for tuning the schema on the fly, improving CPU/ memory usage, caching hit ratio and disk I/O by partitioning, clustering, bucketing, etc.
The tool supports the conversion of business logic into HiveQL, Spark, Python, Scala, Java, SQL, and shell scripts.
The tool supports both syntactical and semantic validation of translated queries and scripts. This ensures automated certification/audit of transformed queries and scripts.
Yes, the tool supports data validation post migration. It can perform validations based on pre-build aggregate functions and can do row by row validation as well.
Yes, it supports all these formats and more.
Yes, it does have an in-built library of keywords, functions, and datatypes that help leverage accelerated transformation of the scripts, and fill the gaps in the target system.
Yes, the tool supports parameterized queries.
Yes, it does support dynamic queries as well.
You can assess a variety of workloads such as BTEQ scripts, SQL queries, application logs, offline query logs or can directly connect to your Teradata database instance.
No. As of now, the tool does not support the automated conversion of macros.