Ques:
You’re working with a dataset of 500 million records, and users are reporting slow queries. How do you optimize query performance and manage storage within SAP Datasphere for such large volumes?
Ans:
Here, query performance and efficient storage management are very crucial. Here’s how we can approach it:
– First, we should choose the right data integration patterns. For larger datasets, we should opt for replicated models, whereas for others, we should opt for federated or hybrid models.
– Apply the concept of table partitioning in the datasphere tables, so that the queries can scan only relevant partitions.
– Ensure that proper indexing is there.
– Design views in such a way that it will push the filters down to the lowest levels.
– All the unnecessary joins and aggregations should be avoided.
– We should also limit the data volume at the query by using parameters and prompts.
– Performing data tiering by moving the cold/rarely used data to a cheaper storage tier.
– Achieving the historical records can also work with various data aging strategies.
– Using snapshot tables or archive layers for rarely accessed records.
– We can also leverage HANA’s native compression features to ensure no unnecessary duplications are present.
– Monitoring the spaces consumed by replicated datasets will also work.
These are the few ways through which we can ensure proper query optimization and performance of queries.
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