Sunkara Yasasvi

So, hello. According to the question asked, a brief introduction about me would be explaining that as a data engineer, I've started my career in 2022 with Snowflake migration projects. So, while working with the migration project, we also worked on migrating the existing data warehouse of Teradata, which was handled by Informatica to Snowflake. Now, this process was handled end-to-end, from the development phase to deployment, by us. In this, we used Snowflake, and that is where my experience with Snowflake and Azure comes from. Before that, I mainly worked with Infosys and was part of a team that handled finance-related applications. So, this is a very brief introduction about myself. On top of that, I have also graduated from Sastry University with a master's degree. It's an integrated degree with a master's in B.Tech in Mechanical and Advanced Manufacturing. A bit more about my work with data engineering in Impetus is that I worked with Snowflake SQL, with Snowflake stored procedures, with tasks, and also with SnowSQL in general for deployment purposes. I also worked with Appworks as a job orchestration tool and with ADF for the storage of data, like static data here. So, if we had to ingest any CSVs or other flat files, we would use ADF and integrate it with Snowflake.

How can you use transaction control to manage data consistency during concurrent ETL processes in Snowflake? So one way we can maintain transactional control is at least one way that we managed to do so is by creating views. So what we did is create the original tables that would have logs entered into them. Let's say we have a table t one that has logs entered. So the same logs would be entered into t 2 and t 3. Now in the off chance that there could be a lock in t one, immediately the data would be entered into t 2. If t 2 isn't locked, then t 3. And on top of that, all these three would be the base tables for a view called table t, and the view table t would have all the execution logs ordered as per its definition. So this way, one thing is that none of the logs would be affected. And in the off chance there is a lock happening, the lock would be handled by exception handling and entering data into the other same kind of tables, like t 2 or t 3. So this way, we would maintain transactional control. That was an innovative way. On top of that, we could also go for granting access or stuff like that.

When we're using ADF and Snowflake to design a data processing pipeline, at least from my point of view, the first thing we would have to consider is how we are ingesting the data. So, once the data is in Snowflake and we are handling that data through a gold architecture, that will be fine. The main problem that I would focus on is how we are ingesting the data from ADF to Snowflake. So my first thought would be to see how that raw data interacts with the bronze layer. So let's say we have jobs that run on a daily basis. Now, something that runs on a daily basis would look for a watermarking process. In the case of a watermarking process, we would ingest small amounts of data per day. Depending on the schedule and how we are maintaining the watermark on a daily basis, we would ingest the data in small amounts, like batches, through batch processing. So this way, we wouldn't have a problem with the initial high volume from raw data. Now, on top of that, if we have to process that amount of data through the bronze to gold architecture, I would think of creating stage and target layers where data would be handled based on watermarking. On top of the target layer, we'd have reporting and everything. So this way, when we're using watermarking, even in the target table, even though it would have a lot of records, those records would be entered based on watermarking.

To ensure data quality and accuracy within Azure Data Factory pipelines, I would use methods such as duplicate checks and watermarking checks. We would implement a Continuous Delivery (CD) situation to handle data quality and accuracy in a pipeline. The reason for this is that we have two types of records: one is a newly entered record and another record which is being updated as part of a previous record. Once a record is entered, it will end up in the target after the ETL process loop. To maintain the quality of the data and its accuracy, we would update the last updated field whenever a record is being updated or deleted. This way, we would have a record of it using either soft delete features or using another field like last updated or something similar.

I am not actually sure what "item put in" means, but my guess for how we could determine item potency would be that, how we ingest the data based on, like, let's say, there is a data the basically, the idea should be that when we are ingesting data, the data should maintain its changing status. Like, if a new record is being entered, it should be entered properly. If there is any change happening, then the record should find its corresponding record in the target and then get updated accordingly. In that case, I would implement streams and tasks. What we would do is implement some kind of an SCD logic using streams, and then go ahead with tasks.

Do you leverage Snowflake's time travel and 0 copy cloning features to enhance data recovery and testing? So, let's say we have 4 environments: devs, SIT, UAT, and production. Okay. So, depending on whether we have the enterprise for production and we have the standard version or something like that. Maybe we have business critical for production, and for the rest, we have enterprise edition. Now in this case, what we would do is we would set time travel retention for production up to 90 days. So, the fail safe would be for 7 days. So this would ensure that we are able to recover data whenever we need. And on top of that, this would help us in case of any outages also. On top of that, when data is in production, let's say a job fails or something, we would be able to recover any data that is lost in that situation, assuming a failsafe hasn't been implemented in the ETL. Now during the time of testing, especially, we could create we could compare before and after an ETL has run, and this way we would easily be able to understand what are the records that have changed, what are the records that have been added, or something like that. So at least this was how I used the time travel feature mainly, especially with a timestamp. So if we have execution logs that mention when something has been executed on. So we could do a before and after a specific query ID has been executed. So like this, we would use

On the provided is no SQL snippet. Can you explain what the issue is with the current stored procedure and how it might affect data processing? Create a update auto begin, update auto set status, where status equals to list. One thing that seems odd to me in this, code is that No. I am excluding that missing commit statement because, when we execute the procedure, it will be committed. That shouldn't be a problem. the main problem that I feel like is the backslashes, which could affect how the data is entered into the

In this code snippet is integrated. There is a target that affects the model deployment. Can you spot and explain the error in the configuration of this model? Materialize table post create index at a time. This user ID. Grant select on So the pre and post hooks, do not seem to be in the way it is set up is that once the index is created, we'll get the access to it. And then it would drop the index. Now in such a situation this user ID. Maybe the problem is that we are trying to grant access on one problem is that we can't create index even though we are using DBT. Let's say we are using this DBT for Snowflake. We wouldn't be able to create indexes. That would be my 1st gripe with this model, and grant select on to role analyst. Because we would that is what seems like a problem is that we wouldn't be able to create an index. As far as I know, indexes are not a thing in Snowflake, using a even if we use a DBT model. Maybe we could create a clustering key.

I would build a machine learning data pipeline, Snowflake, and ensure it is updatable as new data becomes available by using Snowpark along with Snowpipe. Snowpark is primarily used for machine learning data pipelines, so I would set up all the transformations and everything using Snowpark. I would also use Snowpipe in conjunction with streams and tasks to ensure data ingestion is done quickly. Alternatively, we could use Azure Data Factory (ADF) for something else as well. However, this would be a very vague idea of how the data pipeline would be available.

Optimize data retrieval time in Snowflake while dealing with large semi-structured JSON datasets using Snowflake SQL. So one way we could approach this is that when we consider data retrieval time, basically means that we would have to optimize data retrieval time, at least in Snowflake, the first thing that comes to mind is caching. So we would have to find a way to leverage result caching. Let's say we are dealing with JSON datasets, and we need to find a part of the dataset. We need to find at least some micropartitions that we know are being called. Let's say some kind of keys that we know are called on a daily basis, and those keys are static. So in that case, we could cache the keys. We could make sure that they are a part of the result caching. This way, it would be easy for us to call those. It would be quicker for us to call them. We could also implement clustering. And if we are specifically talking about Snowflake SQL, then we could mainly use clustering. We could use result caching, and maybe we could see how the queries are structured.

So, I'm not exactly knowledgeable with DevOps practices, but my idea would be to go around the fact that we could, when it comes to deployment, let's say, when we're using SnowSQL to deploy and we have the scripts, structure the DevOps pipeline in such a way that, whenever any changes are made to the migration script, the migration script immediately executes itself. So the data, whatever changes we've made, are deployed immediately. And on top of that, any redundant testing, like executing, we could set up the DevOps pipeline in such a way that, once you deploy the procedure, the job related to that would immediately be triggered, and this way we could test it in SIT environments.