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SCD Type 2 is used to track historical changes in data by creating new records for changes.
Identify changes in source data
Insert new record with updated data
Update end date of previous record
Add post commands like updating flags or triggers
Example: If a customer changes their address, a new record is created with the updated address while the previous record is marked as expired.
Incremental load process flow involves identifying new/updated data, extracting, transforming, and loading it into the target system.
Identify the source data that has changed since the last load
Extract only the new/updated data from the source system
Transform the data as needed (e.g. applying business rules, data cleansing)
Load the transformed data into the target system, either appending to existing data or updat...
Using PIVOT in SQL to transform input table into expected output
Use the PIVOT keyword followed by the aggregation function and column to pivot on
Specify the values to pivot on as columns in the output table
Include the FOR clause to specify the values to pivot on
Example: SELECT * FROM input_table PIVOT (SUM(value) FOR category IN ('A', 'B', 'C')) AS output_table
Query optimization involves steps to improve the performance of database queries.
Identify slow queries using tools like query logs or profiling.
Analyze query execution plans to understand how queries are being processed.
Optimize queries by adding indexes, rewriting queries, or restructuring data.
Consider factors like data distribution, join types, and query complexity.
Test and benchmark optimized queries to ensure...
Dimension tables store descriptive attributes while fact tables store quantitative data. Dimension tables are loaded first.
Dimension tables contain attributes like customer name, product category, etc.
Fact tables contain quantitative data like sales revenue, quantity sold, etc.
Dimension tables are typically loaded first as they provide context for the quantitative data in fact tables.
I appeared for an interview before Feb 2024.
Using PIVOT in SQL to transform input table into expected output
Use the PIVOT keyword followed by the aggregation function and column to pivot on
Specify the values to pivot on as columns in the output table
Include the FOR clause to specify the values to pivot on
Example: SELECT * FROM input_table PIVOT (SUM(value) FOR category IN ('A', 'B', 'C')) AS output_table
Incremental load process flow involves identifying new/updated data, extracting, transforming, and loading it into the target system.
Identify the source data that has changed since the last load
Extract only the new/updated data from the source system
Transform the data as needed (e.g. applying business rules, data cleansing)
Load the transformed data into the target system, either appending to existing data or updating e...
Query optimization involves steps to improve the performance of database queries.
Identify slow queries using tools like query logs or profiling.
Analyze query execution plans to understand how queries are being processed.
Optimize queries by adding indexes, rewriting queries, or restructuring data.
Consider factors like data distribution, join types, and query complexity.
Test and benchmark optimized queries to ensure perf...
SCD Type 2 is used to track historical changes in data by creating new records for changes.
Identify changes in source data
Insert new record with updated data
Update end date of previous record
Add post commands like updating flags or triggers
Example: If a customer changes their address, a new record is created with the updated address while the previous record is marked as expired.
Dimension tables store descriptive attributes while fact tables store quantitative data. Dimension tables are loaded first.
Dimension tables contain attributes like customer name, product category, etc.
Fact tables contain quantitative data like sales revenue, quantity sold, etc.
Dimension tables are typically loaded first as they provide context for the quantitative data in fact tables.
I applied via Job Portal and was interviewed in Mar 2024. There was 1 interview round.
On the sql, python and other puzzles
Top trending discussions
I applied via Naukri.com and was interviewed in Jul 2024. There were 2 interview rounds.
Use a stored procedure to prompt user for input in ETL process.
Create a stored procedure with parameters to accept user input.
Use the parameters in the stored procedure to filter or manipulate data.
Prompt the user for input values when calling the stored procedure.
Use TRY...CATCH block in SQL to handle errors in Stored Procedures
Enclose the code inside a TRY block
Use a CATCH block to handle any errors that occur
Use RAISEERROR or THROW statement to raise custom error messages
Use @@ERROR or @@ROWCOUNT to check for errors or affected rows
I appeared for an interview before Jun 2024, where I was asked the following questions.
ETL Developer must handle data extraction, transformation, and loading efficiently to ensure data integrity and performance.
Understand source systems: Know where data is coming from, e.g., databases, APIs.
Data transformation: Apply business rules, e.g., converting date formats.
Loading data: Efficiently load data into target systems, e.g., data warehouses.
Error handling: Implement logging and error handling mechanisms.
P...
I applied via Referral and was interviewed before Jul 2020. There were 3 interview rounds.
IDT has made significant progress in the development of CRISPR-based gene editing tools.
IDT has launched a new CRISPR enzyme called Alt-R Cas12a (Cpf1) that can target AT-rich regions of the genome.
IDT has also developed a new CRISPR-Cas9 system that allows for multiplexed gene editing.
IDT has expanded its portfolio of PrimeTime qPCR assays for gene expression analysis.
IDT has introduced a new line of xGen Lockdown Pan...
I applied via Recruitment Consulltant and was interviewed before May 2021. There was 1 interview round.
I applied via Referral and was interviewed before Dec 2020. There were 4 interview rounds.
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