Extract, transform, and load is what’s meant by the acronym ETL. These three functionalities of databases are merged into a single tool so that you can take data out of one database and store it or maintain it in another database. This allows you to make use of the flexibility that databases offer. This post provides a collection of questions that have been compiled that are asked during interviews on the majority of occasions. Study the following list of ETL scenario interview questions, and prepare to ace your upcoming employment interview.
1. What Exactly Is ETL?
The term “Extract, Transform, and Load” (ETL) describes the three processes involved in combining data from various sources. These three database operations—Extract, Transform, and load—are rolled into one convenient tool for migrating information from one database to another. The data is compiled from many, usually several, sources during the extract phase. Here, the information is transformed into a format suitable for incorporation into another database. Loading is the process of adding information to the new database. Integrating transaction data from a warehouse and preparation for human consumption makes ETL a tried-and-true technology on which many modern enterprises and organizations rely. It is frequently used to acquire and integrate external partners’ data and consolidate information resulting from corporate mergers.
2. What Exactly Do The Terms “Initial Load” And “Full Load” Mean?
The term “Initial Load” describes the first pass at importing data into the Data Mart from various sources. In contrast, a Full Load can be quickly implemented and assures full synchronization with minimal effort when applied to a small dataset.
3. What Are The Three Tiers That Are Involved In ETL?
The majority of data warehouses are organized in a three-tier hierarchy. The information is stored in the first layer, a compilation of data obtained from various external sources. In the second layer, known as the integration layer, data is modified so that it may better serve the requirements of the business. The third layer is known as the dimension layer, and it is where information that is used internally is stored.
4. What Exactly Are Snapshots, And What Distinguishing Qualities Do They Have?
Snapshots are read-only reproductions of the data contained in a master table. They can be utilized for tracking actions, such as the time at which an occurrence took place, a key to identify the picture, and data relevant to the key. They are stored on distant nodes and perform periodic refreshes to ensure that changes to the table are accurately reflected.
5. Give An Explanation Of The Distinctions Between An Unconnected Lookup And A Connected One.
In connected lookup, many columns can be returned from the same row or stored in the dynamic lookup cache. On the other hand, in unconnected lookup, one return port is designated, and only one column is returned from each row. In contrast, unconnected lookup is only employed when the lookup function is used instead of an expression transformation while mapping. Connected lookup is an integral part of the mapping process itself. In contrast, only one output port is provided by an unconnected lookup, which makes it possible to return numerous results using a connected lookup. In comparison, unconnected lookup does not allow for user-defined default values to be used, whereas connected lookup does. When conducting a connected lookup, either static or dynamic cache may be utilized; however, when performing an unconnected lookup, only static cache may be used.
6. Give An Explanation Of The Term “Partitioning,” As Well As The Terms “Hash Partitioning” And “Round-Robin Partitioning.”
The process of subdividing an area of data storage to improve its performance is known as partitioning. Round-robin and hash partitions are the two types of partitions. The adaptive server distributes rows to the various partitions in a round-robin fashion in round-robin partitioning. This ensures that each division has roughly the same number of rows, which allows for load balancing to be done. In hash partitioning, each partition is assigned a hash key to determine the distribution of rows.
7. How Does ETL Conduct Its Table Analysis?
You can validate the structures of objects within the system using the statement known as ANALYZE. You can calculate the most effective strategy for data retrieval by reusing the statistics obtained by that statement in a cost-based optimizer. COMPUTER, ESTIMATE, and DELETE are some examples of other operations.
8. How Can The Mapping Be Adjusted More Precisely In ETL?
Utilizing the condition for the filter in the source, qualifying the data even without the filter, making use of cache store and persistence in lookup t/r, utilizing the aggregations t/r in sorted i/p group by different ports, and increasing the cache size and commit interval are all aspects of fine-tuning the mapping. Alternatively, you can substitute operators for function calls within expressions.
9. What Exactly Is Meant By The Term “Incremental Loading”?
A method that utilizes fractional loading is known as the ETL Incremental Loading methodology. It reduces the amount of data you add or alter, which decreases the number of things that could need to be fixed if there is an irregularity. There is a direct correlation between the amount of data validated and the amount of time spent doing so.
10. How Is ETL Incremental Loading Different From ETL Full Loading?
When performing an ETL Full Load, the entire dataset is deleted, and a new one is loaded in its place. as a result, it doesn’t necessitate the keeping of any additional data, such as timestamps.
With ETL Incremental Loading, you transfer only the data from the source that has been modified since the target system was last updated. Two distinct types of Incremental Loading exist, each of which is defined by the quantity of data being loaded:
- Data can be loaded in small increments using the stream incremental load method.
- Batch Incremental Load is used to load large amounts of data at once.
11. In What Situations Would You Benefit From Employing ETL Incremental Loading?
The following scenarios benefit greatly from ETL Incremental Loading as opposed to ETL Full Loading:
- Having to deal with a significantly more extensive data set
- Problems with querying data speed due to data size and technical constraints.
- You may monitor the status of the data as it evolves.
- Many databases periodically purge historical data. You may want to keep the erased information in the target system, such as a Data Warehouse.
12. What Exactly Is A “Staging” Area, And Why Is It Required?
In ETL processes, staging is an optional intermediate storage place. There are four options for deciding whether to stage or not to stage:
- Auditing requirements. Thanks to the staging area, we may compare the original input file to our result. It’s especially beneficial when the source system overwrites the history (e.g., flat files on an FTP server are being overwritten daily.)
- Recovery requirements. Even though PCs are becoming faster and have greater bandwidth in almost every form, some legacy systems and environments are still challenging to extract data from. It is best to save the data as soon as it is removed from the source system. In this manner, staging objects can serve as recovery checkpoints, avoiding the circumstance in which a process must be repeated entirely when it fails at 90% completion.
- Backup. The staging area can be used to recover data in a target system in the event of a failure.
- Performance under load. Staging is the way to proceed if the data must be loaded into the system as quickly as feasible. Developers load data into the staging area as is, then execute various transformations on it. It’s significantly more efficient than converting the data on the fly before putting it into the target system, but it requires more disc space.
13. How Would You Plan For And Create Incremental Loads?
The most frequent method for preparing for incremental load is to use the date and time a record was added or updated. Depending on business logic, it can be created during the initial load and then maintained, or it can be introduced later in an ETL process. It is critical to ensure that the fields utilized for this are not altered during the procedure and can be trusted. The next stage is to pick how to capture the changes, but the fundamentals are always the same: compare the latest updated date to the target’s maximum date and then take all more extensive records. Another solution is to create a delta load procedure that compares existing records with new ones and only loads the differences. On the other hand, this is not the most effective approach.
14. What Is The Advantage Of Third-Party Tools Such As SSIS Over SQL Scripts?
Third-party tools make development faster and easier. Because of their graphical user interfaces, these tools may also be utilized by persons who are not technical specialists but have a broad understanding of the industry. ETL tools can produce metadata automatically and have predefined connectors for most sources. One of the essential features is the ability to merge data from various files automatically.
15. What Are The Drawbacks Of Indexes?
Indexes enable rapid lookups but degrade load performance: DML operations, like inserts and updates, will be unable to perform on heavily indexed tables. It should be noted that indexes use additional disc space. Worse, the database back end must update all relevant indexes whenever data changes. It also adds overhead due to index fragmentation: developers or DBAs must handle index maintenance, reorganization, and rebuilds. Index fragmentation harms performance. When new data is added to an index, the database engine must make room for it. The latest data input may disrupt the present order—the SQL engine may split the data from a single data page, resulting in excessive free space (internal fragmentation). It may also destabilize the existing page order, forcing the SQL engine to hop between pages when reading data from the disc. This adds extra expense to the data reading process and causes random disc I/O. When index fragmentation is between 5 and 30 percent, Microsoft recommends restructuring the index and rebuilding it when it is more significant than 30 percent. In SQL Server, an index rebuild produces a new index beneath the existing one and then replaces it. Rebuilding may prevent the entire table from reading it (when using an edition other than their Enterprise offering.) Index restructuring is essentially rearranging leaf pages and attempting to compress data pages.
16. Which Is Preferable In Terms Of Performance: Filtering Data First And Then Joining It With Other Sources, Or Joining Data First And Then Filtering?
Filtering data first and then joining it with data from other sources is preferable. Getting rid of useless data as early as possible is an excellent strategy to increase ETL process performance. It reduces the time spent on data transport, I/O, and memory processing. The general rule is to decrease the number of processed rows and avoid altering data that will never be used.
17. How Would You Set Up Logging For The ETL Process?
Logging is critical for keeping track of all changes and failures during a load. The most typical methods for preparing for logging are flat files or a logging table. Counts, timestamps, and metadata about the source and target are added during the process and then dumped into a flat file or table. The load can then be verified for invalid runs in this manner. When such a table or file is found, the next step is to create notifications. This could be a report or a primary structured email that describes the load as soon as it is completed (e.g., the number of processed records compared to the previous load.)
18. What Does Data Profiling In An ETL Process Accomplish? What Are The Most Critical Steps In The Data Profiling Process?
Data profiling tasks aid in the preservation of data quality. Several difficulties were identified and resolved during this period. The most significant are:
- A row’s keys and unique identifier. The rows that will be inserted must be distinct. Businesses frequently utilize natural keys to identify a specific row, but developers must ensure this is sufficient.
- Types of data Column names that suggest a specific category should be investigated: Will the specified type alter the meaning of the column or allow for data loss? Data types can also impact post-ETL performance: Even if it doesn’t matter much throughout the process, text loaded into a variable-length string column will suffer a performance cost when users begin querying the target on some RDBMSes.
- Relationships between data. It is critical to understand how tables relate to one another. To avoid losing essential structural information, additional modeling may be required to link some parts of the data. Another consideration is the cardinality of a relationship, which defines how the tables involved will be connected in the future.
19. What Are The Three Methods For Implementing Row Versioning?
Maintaining row history necessitates the implementation of a versioning policy. The three most common types are as follows:
- Add a new record: In this situation, updated information about the row is saved, but it isn’t related to anything else—treated it’s as a new row. In this instance, there is usually an additional column (or more) to readily identify the most recent modification. It might be a “current record” flag, a “cause for change” text field, or a “valid from/until” DateTime pair (or strange, perhaps).
- Column(s) not included: In this case, the old value of a modified column is relocated to an additional column (e.g., old amount), and the new value replaces the original (e.g., amount.)
- Table of Contents: First, a second history table is constructed from the primary table. Then we have several possibilities for loading data into this table. Creating DML triggers is one of them. RDBMS vendor functionality, such as changing data capture features, can be helpful in this situation. Such features can be significantly more efficient than triggers, for example, by immediately tracking changes in the transaction log, which is in charge of storing information about any changes made to the database. SQL Server, mainly SQL Server 2016 and later, can track changes using system-versioned temporal tables. This feature keeps a complete history table alongside the most recent one: The primary temporal table only stores the most current version of the data, but it is linked to the history table, which keeps all earlier versions.
20. Describe The ETL Testing Operations.
ETL testing entails the following:
- Check if the data is being transformed correctly, following the business requirements.
- Check that the anticipated data is put into the warehouse without truncation or loss.
- Check that the ETL program reports incorrect data and replaces it with default values.
- To optimize scalability and performance, ensure that data loads within the required time window.
21. Could You Please Explain The Meaning Of The Terms “Extraction,” “Transformation,” And “Loading”?
Extraction: Moved data from an external source to the Data Warehouse pre-processor database. Transformation: The transform data job enables the generation, modification, and transformation of point-to-point data. Loading: This task involves adding data to a warehouse’s database table.
22. Mention The Various Types Of Data Warehouse Applications And The Distinction Between Data Mining And Data Warehousing.
The various sorts of data warehouse applications are as follows:
- Analytical Processing of Information
- Data Exploration
- Data mining is extracting hidden predictive information from massive databases and interpreting the data. In contrast, data warehousing may use a data mine for faster analytical processing of the data. Data warehousing is the process of combining data from several sources into a single repository.
23. What Are The Tiers In ETL Called?
When working with ETL, the first layer is called the source layer, where data is initially loaded. Following its transformation, information is stored in the second layer, known as the integration layer. The presentation layer itself sits atop the third layer, the dimension layer.
24. Please Explain Why A Datareader Destination Adapter Would Be Beneficial.
The DataReader Destination Adapter is helpful because it exposes the data from the DataFlow task by implementing the DataReader interface and populates an ADO record set (containing records and columns) in memory.
25. What Are The Benefits Of ETL Testing?
ETL testing is essential because:
- To maintain track of the data being transmitted from one system to another.
- To monitor the process’s efficiency and speed.
- To become well-versed in the ETL procedure before using it in your business and production.
We hope that these ETL interview questions and answers will assist you in preparing for and participating in interviews. Be confident and be you. Lastly, good luck!