ETL Interview Question Bank

Transcription

1 ETL Interview Question Bank Author: - Sheetal Shirke Version: - Version 0.1

2 ETL Architecture Diagram 1 ETL Testing Questions 1. What is Data WareHouse? A data warehouse (DW or DWH), also known as an enterprise data warehouse (EDW), is a system used for reporting and data. DWs are central repositories of integrated data from one or more disparate sources. They store current and historical data and are used for creating analytical reports for knowledge workers throughout the enterprise (Refer Diagram 1) 2. Difference between Dataware House and Data Mart? Refer Diagram 1 Data mart and data warehousing are tools to assist management to come up with relevant information about the organization at any point of time While data marts are limited for use of a department only, data warehousing applies to an entire organization Data marts are easy to design and use while data warehousing is complex and difficult to manage Data warehousing is more useful as it can come up with information from any department 3. What is ETL/Data Warehouse Testing? ETL stands for Extract Transformation and Load, It collect the different source data from Heterogeneous System (DB), Transform the data into Data warehouse (Target) At the Time of Transformation, Data are first transform to Staging Table (temporary table)

3 Based on Business rules the data are mapped into target table, this process are manually mapped / we configure using ETL Tool. ETL not transformed the Duplicate data Data Transformation process speed based on Source and Target Data ware House We need to consider the OLAP(Online Analytic Processing) Structure.Data warehouse Model Source data consist of (XML, Flat file,database.excel Report.Dataware House We need to set the validation at time of data transformation like Avoid the NULL values in the table, validate the data type as using Tiny int instead of integer.etc Based on the user requirement, ETL process starts. 4. Explain what are the ETL testing operations include? Verify whether the data is transforming correctly according to business requirements Verify that the projected data is loaded into the data warehouse without any truncation and data loss Make sure that ETL application reports invalid data and replaces with default values Make sure that data loads at expected time frame to improve scalability and performance 5. What are the various tools used in ETL. Cognos Decision Stream Oracle Warehouse Builder/Oracle Data Integrator Business Objects XI SAS business warehouse SAS Enterprise ETL server 6. What are staging area in ETL testing and its purpose? Staging area is place where you hold temporary tables on data warehouse server. Staging tables are connected to work area or fact tables. We basically need staging area to hold the data, and perform data cleansing and merging, before loading the data into warehouse. 7. What is Primary, Foreign keys and difference among them? Primary Key: A primary key is a field or combination of fields that uniquely identify a record in a table, so that an individual record can be located without confusion. Foreign Key: A foreign key (sometimes called a referencing key) is a key used to link two tables together. Typically you take the primary key field from one table and insert it into the other table where it becomes a foreign key (it remains a primary key in the original table).

4 8. What is Surrogate Key? Surrogate key is a substitution for the natural primary key. It is just a unique identifier or number for each row that can be used for the primary key to the table. The only requirement for a surrogate primary key is that it is unique for each row in the table. Data warehouses typically use a surrogate, (also known as artificial or identity key), key for the dimension tables primary keys. 9. What is Fact and Dimensions? Fact: Facts are the metrics that business users would use for making business decisions. Generally, facts are mere numbers. The facts cannot be used without their dimensions Dimension: Dimensions are those attributes that qualify facts. They give structure to the facts. Dimensions give different views of the facts. The facts & Dimension tables are linked by means of key called surrogate keys. Each fact table would have a column surrogate key that would have a corresponding key in the dimension tables. 10. Types of Dimensions. Slowly Changing Dimensions: Attributes of a dimension that would undergo changes over time. It depends on the business requirement whether particular attribute history of changes should be preserved in the data warehouse. This is called a Slowly Changing Attribute and a dimension containing such an attribute is called a Slowly Changing Dimension. Rapidly Changing Dimensions: A dimension attribute that changes frequently is a Rapidly Changing Attribute. If you don t need to track the changes, the Rapidly Changing Attribute is no problem, but if you do need to track the changes, using a standard Slowly Changing Dimension technique can result in a huge inflation of the size of the dimension. One solution is to move the attribute to its own dimension, with a separate foreign key in the fact table. This new dimension is called a Rapidly Changing Dimension. Junk Dimensions: A junk dimension is a single table with a combination of different and unrelated attributes to avoid having a large number of foreign keys in the fact table. Junk dimensions are often created to manage the foreign keys created by Rapidly Changing Dimensions.

5 Inferred Dimensions: While loading fact records, a dimension record may not yet be ready. One solution is to generate a surrogate key with Null for all the other attributes. This should technically be called an inferred member, but is often called an inferred dimension. Conformed Dimensions: A Dimension that is used in multiple locations is called a conformed dimension. A conformed dimension may be used with multiple fact tables in a single database, or across multiple data marts or data warehouses. Degenerate Dimensions: A degenerate dimension is when the dimension attribute is stored as part of fact table, and not in a separate dimension table. These are essentially dimension keys for which there are no other attributes. In a data warehouse, these are often used as the result of a drill through query to analyze the source of an aggregated number in a report. You can use these values to trace back to transactions in the OLTP system. Role Playing Dimensions: A role-playing dimension is one where the same dimension key along with its associated attributes can be joined to more than one foreign key in the fact table. For example, a fact table may include foreign keys for both Ship Date and Delivery Date. But the same date dimension attributes apply to each foreign key, so you can join the same dimension table to both foreign keys. Here the date dimension is taking multiple roles to map ship date as well as delivery date, and hence the name of Role Playing dimension. Shrunken Dimensions: A shrunken dimension is a subset of another dimension. For example, the Orders fact table may include a foreign key for Product, but the Target fact table may include a foreign key only for Product Category, which is in the Product table, but much less granular. Creating a smaller dimension table, with Product Category as its primary key, is one way of dealing with this situation of heterogeneous grain. If the Product dimension is snowflake, there is probably already a separate table for Product Category, which can serve as the Shrunken Dimension. Static Dimensions: Static dimensions are not extracted from the original data source, but are created within the context of the data warehouse. A static dimension can be loaded manually for example with Status codes or it can be generated by a procedure, such as a Date or Time dimension. 11. Types of Facts. Additive: Additive facts are facts that can be summed up through all of the dimensions in the fact table. A sales fact is a good example for additive fact.

6 Semi-Additive: Semi-additive facts are facts that can be summed up for some of the dimensions in the fact table, but not the others. Eg: Daily balances fact can be summed up through the customers dimension but not through the time dimension. Non-Additive: Non-additive facts are facts that cannot be summed up for any of the dimensions present in the fact table. Eg: Facts which have percentages, ratios calculated. Factless Fact Table: In the real world, it is possible to have a fact table that contains no measures or facts. These tables are called Factless Fact tables. Eg: A fact table which has only product key and date key is a factless fact. There are no measures in this table. But still you can get the number products sold over a period of time. Based on the above classifications, fact tables are categorized into two: Cumulative: This type of fact table describes what has happened over a period of time. For example, this fact table may describe the total sales by product by store by day. The facts for this type of fact tables are mostly additive facts. The first example presented here is a cumulative fact table. Snapshot: This type of fact table describes the state of things in a particular instance of time, and usually includes more semi-additive and non-additive facts. The second example presented here is a snapshot fact table. 12. What SCD (Slowly Changing Dimensions) and its types? Slowly Changing Dimensions (SCD) - dimensions that change slowly over time, rather than changing on regular schedule, time-base. In Data Warehouse there is a need to track changes in dimension attributes in order to report historical data. In other words, implementing one of the SCD types should enable users assigning proper dimensions attribute value for given date? Example of such dimensions could be: customer, geography, and employee. There are many approaches how to deal with SCD. The most popular are: Type 0 - The passive method Type 1 - Overwriting the old value Type 2 - Creating a new additional record Type 3 - Adding a new column

7 Type 4 - Using historical table Type 6 - Combine approaches of types 1, 2, 3 (1+2+3=6) Type 0 - The passive method. In this method no special action is performed upon dimensional changes. Some dimension data can remain the same as it was first time inserted, others may be overwritten. Type 1 - Overwriting the old value. In this method no history of dimension changes is kept in the database. The old dimension value is simply overwritten be the new one. This type is easy to maintain and is often use for data which changes are caused by processing corrections (e.g. removal special characters, correcting spelling errors). Before the change: Customer_ID Customer_Name Customer_Type 1 Cust_1 Corporate After the change: Customer_ID Customer_Name Customer_Type 1 Cust_1 Retail Type 2 - Creating a new additional record. In this methodology all history of dimension changes is kept in the database. You capture attribute change by adding a new row with a new surrogate key to the dimension table. Both the prior and new rows contain as attributes the natural key (or other durable identifier). Also 'effective date' and 'current indicator' columns are used in this method. There could be only one record with current indicator set to 'Y'. For 'effective date' columns, i.e. start_date and end_date, the end_date for current record usually is set to value Introducing changes to the dimensional model in type 2 could be very expensive database operation so it is not recommended to use it in dimensions where a new attribute could be added in the future. Before the change: Customer_ID Customer_Name Customer_Type Start_Date End_Date Current_Flag 1 Cust_1 Corporate Y After the change: Customer_ID Customer_Name Customer_Type Start_Date End_Date Current_Flag 1 Cust_1 Corporate N 2 Cust_1 Retail Y Type 3 - Adding a new column. In this type usually only the current and previous value of dimension is kept in the database. The new value is loaded into 'current/new' column and the old one into 'old/previous' column. Generally speaking the history is limited to the number of column created for storing historical data. This is the least commonly needed technique.

8 Before the change: Customer_ID Customer_Name Current_Type Previous_Type 1 Cust_1 Corporate Corporate After the change: Customer_ID Customer_Name Current_Type Previous_Type 1 Cust_1 Retail Corporate Type 4 - Using historical table. In this method a separate historical table is used to track all dimensions attribute historical changes for each of the dimension. The 'main' dimension table keeps only the current data e.g. customer and customer_history tables. Current table: Customer_ID Customer_Name Customer_Type 1 Cust_1 Corporate Historical table: Customer_ID Customer_Name Customer_Type Start_Date End_Date 1 Cust_1 Retail Cust_1 Other Cust_1 Corporate Type 6 - Combine approaches of types 1, 2, 3 (1+2+3=6). In this type we have in dimension table such additional columns as: current_type - for keeping current value of the attribute. All history records for given item of attribute have the same current value. historical_type - for keeping historical value of the attribute. All history records for given item of attribute could have different values. start_date - for keeping start date of 'effective date' of attribute's history. end_date - for keeping end date of 'effective date' of attribute's history. current_flag - for keeping information about the most recent record. In this method to capture attribute change we add a new record as in type 2. The current_type information is overwritten with the new one as in type 1. We store the history in a historical_column as in type 3. Customer_I D Customer_Nam e 1 Cust_1 Corporate Retail 2 Cust_1 Corporate Other Current_Type Historical_Type Start_Date End_Date Current_Flag N N

9 3 Cust_1 Corporate Corporate Y 13. What are OLTP and OLAP? OLTP (On-line Transaction Processing) is characterized by a large number of short on-line transactions (INSERT, UPDATE, and DELETE). The main emphasis for OLTP systems is put on very fast query processing, maintaining data integrity in multi-access environments and an effectiveness measured by number of transactions per second. In OLTP database there is detailed and current data, and schema used to store transactional databases is the entity model (usually 3NF). OLAP (On-line Analytical Processing) is characterized by relatively low volume of transactions. Queries are often very complex and involve aggregations. For OLAP systems a response time is an effectiveness measure. OLAP applications are widely used by Data Mining techniques. In OLAP

10 database there is aggregated, historical data, stored in multi-dimensional schemas (usually star schema). The following table summarizes the major differences between OLTP and OLAP system design. Source of data Purpose of data OLTP System Online Transaction Processing (Operational System) Operational data; OLTPs are the original source of the data. To control and run fundamental business tasks What the data Reveals a snapshot of ongoing business processes Inserts and Updates Queries Processing Speed Space Requirements Database Design Backup and Recovery Short and fast inserts and updates initiated by end users Relatively standardized and simple queries Returning relatively few records Typically very fast Can be relatively small if historical data is archived Highly normalized with many tables Backup religiously; operational data is critical to run the business, data loss is likely to entail significant monetary loss and legal liability OLAP System Online Analytical Processing (Data Warehouse) Consolidation data; OLAP data comes from the various OLTP Databases To help with planning, problem solving, and decision support Multi-dimensional views of various kinds of business activities Periodic long-running batch jobs refresh the data Often complex queries involving aggregations Depends on the amount of data involved; batch data refreshes and complex queries may take many hours; query speed can be improved by creating indexes Larger due to the existence of aggregation structures and history data; requires more indexes than OLTP Typically de-normalized with fewer tables; use of star and/or snowflake schemas Instead of regular backups, some environments may consider simply reloading the OLTP data as a recovery method 14. What is Grain of Fact OR Fact Granularity? Level of granularity means level of detail that you put into the fact table in a data warehouse. For example: Based on design you can decide to put the sales data in each transaction. Now, level of granularity would mean what detail you are willing to put for each

11 transactional fact. Product sales with respect to each minute or you want to aggregate it up to minute and put that data. It also means that we can have (for example) data aggregated for a year for a given product as well as the data can be drilled down to Monthly, weekly and daily basis...the lowest level is known as the grain. Going down to details is Granularity 15. STAR Schema and Snowflake Schema? STAR Schema: Star Schema has single fact table connected to dimension tables and it visualize as a star. In star schema only one link establishes the relationship between the fact table and any of the dimension tables. SNOW-FLAKE Schema: Snowflake Schema is an extension of the star schema. In this model, dimension tables are not necessarily fully flattened. Here, very large dimension tables are normalized into multiple sub dimensional tables. It is used when a dimensional table becomes very big. Also, every dimension table is associated with sub dimension table and has multiple links.

12 16. What are Transformation and its types? Aggregator Transformation Application Source Qualifier Transformation Custom Transformation Data Masking Transformation Expression Transformation External Procedure Transformation Filter Transformation HTTP Transformation Input Transformation Java Transformation Joiner Transformation Lookup Transformation Normaliser Transformation Output Transformation Rank Transformation Reusable Transformation Router Transformation Sequence Generator Transformation Sorter Transformation Source Qualifier Transformation SQL Transformation Stored Procedure Transformation Transaction Control Transaction

13 Union Transformation Unstructured Data Transformation Update Strategy Transformation XML Generator Transformation XML Parser Transformation XML Source Qualifier Transformation Advanced External Procedure Transformation External Transformation 17. Explain what is the difference between OLAP tools and ETL tools? ETL tool is meant for extraction data from the legacy systems and load into specified data base with some process of cleansing data. Eg: Informatica, data stage...etc OLAP is meant for Reporting purpose. In OLAP data available in Multidimensional model. So that u can write simple query to extract data from the data base. Eg: Business objects, Cognos...etc 18. Explain these terms Session, Worklet, Mapplet and Workflow? Mapplet : It arranges or creates sets of transformation Worklet: It represents a specific set of tasks given Workflow: It s a set of instructions that tell the server how to execute tasks Session: It is a set of parameters that tells the server how to move data from sources to target 19. What are Data ware House and Data Mining? Data mining is the process of finding patterns in a given data set. These patterns can often provide meaningful and insightful data to whoever is interested in that data. Data mining is used today in a wide variety of contexts in fraud detection, as an aid in marketing campaigns, and even supermarkets use it to study their consumers. Data warehousing can be said to be the process of centralizing or aggregating data from multiple sources into one common repository. 20. What are the basic checks done during ETL Testing? Reconciliation testing: Sometimes, it is also referred as Source to Target count testing. In this check, matching of count of records is checked. Although this is not the best way, but in case of time crunch, it helps.

14 Eg: Constraint testing: Here test engineer, maps data from source to target and identify whether the data is mapped or not. Following are the key checks: UNIQUE, NULL, NOT NULL, Primary Key, Foreign key, DEFAULT, CHECK Validation testing (source to target data): It is generally executed in mission critical or financial projects. Here, test engineer, validates each data point and match source to target data. Testing for duplicate check: It is done to ensure that there are no duplicate values for unique columns. Duplicate data can arise due to any reason like missing primary key etc. Below is one Eg: Testing for attribute check: To check if all attributes of source system are present in target table. Logical or transformation testing: To test any logical gaps in the. Here, depending upon the scenario, following methods can be used: boundary value analysis, equivalence partitioning, comparison testing, error guessing or sometimes, graph based testing methods. It also covers testing for look-up conditions. Incremental and historical data testing: Test to check the data integrity of old & new data with the addition of new data. It also covers the validation of purging policy related scenarios. GUI / navigation testing: To check the navigation or GUI aspects of the front end reports. Note: In case of ETL or data warehouse testing, re-testing or regression testing is also part of this effort. Their concept / definition remain the same.

15 21. What is Active and Passive Transformation? List various types of them. If something is changing for the row in a transformation then it s an active Transformation. But what is changing? A transformation that changes the number of rows passing through it. Changing the order of the rows passing through it also consider in active transformation. When a row enters a transformation, Informatica assigns a row number. If this number change for a row, that's an Active transformation. In other words the nth row coming in will go as n'th row, and then the transformation is Passive Filter Transformation: The number of rows getting in the transformation and coming out is different. And as specified above it satisfies the criteria for being an active transformation. But this is not the case, if all the rows in filter transformation will satisfy the True filter condition then it s behave as a Passive Transformation. Aggregator Transformation: Aggregator transformation is used to get the aggregate value based on the group by ports. Thus if we have duplicates on the group by columns then it will pass only the distinct records. So here also the records coming into the transformation and going out are different and acts as an active transformation. But similar to filter transformation here also there can be an exception. That is if there are no duplicates on the group by ports then all the rows will be passed. Sorter Transformation: This is one transformation which can satisfy both the criteria of being an active transformation. The sorter transformation is also provided to output only the distinct rows, where it can filter the duplicate rows and send the unique set. As it sorts the data so the order of the rows changes which satisfy our second criteria. Union Transformation: This is a transformation which becomes active only due to second criteria. In the union transformation the order of rows always not same as it came from source. Unlike Joiner transformation which restricts the data flow from one source until it gets all the data from the other source and so the order of the rows doesn t change. While on other hand in Union transformation it does not restrict the flow of any data and keeps on passing the data as it receives. So the order of the rows keeps on changing satisfying the second criteria for being an active transformation.

16 Router Transformation: Router Transformation becomes active Due to filter condition we are changing the input rows and output rows. For multiple groups if condition satisfies for more than one group the we will send the data in multiple output transformation. So for example we get 50 rows and we have 4 groups with a condition TRUE then all the groups will pass 50 rows that is total of 200 rows will come out of the Router making it an active transformation. 22. What is erroneous data in ETL testing? Data which fails to satisfy the business rules is called as Erroneous Data. This data is captured in Error Table during transformation for further analysis. Database related questions: 1. What are joins and its types?

17 SQL JOIN is a method to retrieve data from two or more database tables. a. JOIN or INNER JOIN b. OUTER JOIN i. LEFT OUTER JOIN or LEFT JOIN ii. RIGHT OUTER JOIN or RIGHT JOIN iii. FULL OUTER JOIN or FULL JOIN c. NATURAL JOIN d. CROSS JOIN e. SELF JOIN JOIN or INNER JOIN: In this kind of a JOIN, we get all records that match the condition in both the tables, and records in both the tables that do not match are not reported. In other words, INNER JOIN is based on the single fact that: ONLY the matching entries in BOTH the tables SHOULD be listed. Note that a JOIN without any other JOIN keywords (like INNER, OUTER, LEFT, etc) is an INNER JOIN. In other words, INNER JOIN is a Syntactic sugar for JOIN OUTER JOIN: OUTER JOIN retrieves either, the matched rows from one table and all rows in the other table OR, all rows in all tables (it doesn't matter whether or not there is a match). There are three kinds of Outer Join: LEFT OUTER JOIN or LEFT JOIN: This join returns all the rows from the left table in conjunction with the matching rows from the right table. If there are no columns matching in the right table, it returns NULL values. RIGHT OUTER JOIN or RIGHT JOIN: This JOIN returns all the rows from the right table in conjunction with the matching rows from the left table. If there are no columns matching in the left table, it returns NULL values. FULL OUTER JOIN or FULL JOIN: This JOIN combines LEFT OUTER JOIN and RIGHT OUTER JOIN. It returns row from either table when the conditions are met and returns NULL value when there is no match.

18 In other words, OUTER JOIN is based on the fact that : ONLY the matching entries in ONE OF the tables (RIGHT or LEFT) or BOTH of the tables(full) SHOULD be listed. Note that `OUTER JOIN` is a loosened form of `INNER JOIN`. NATURAL JOIN: It is based on the two conditions: The JOIN is made on all the columns with the same name for equality. Removes duplicate columns from the result. This seems to be more of theoretical in nature and as a result (probably) most DBMS don't even bother supporting this. CROSS JOIN: It is the Cartesian product of the two tables involved. The result of a CROSS JOIN will not make sense in most of the situations. Moreover, we won t need this at all (or needs the least, to be precise). SELF JOIN: It is not a different form of JOIN; rather it is a JOIN (INNER, OUTER, etc) of a table to itself. JOINs based on Operators Depending on the operator used for a JOIN clause, there can be two types of JOINs. They are Equi JOIN and Theta JOIN Equi JOIN: For whatever JOIN type (INNER, OUTER, etc), if we use ONLY the equality operator (=), then we say that the JOIN is an EQUI JOIN. Theta JOIN: This is same as EQUI JOIN but it allows all other operators like >, <, >= etc. 2. What is difference between delete, truncate and drop command? DELETE: The DELETE command is used to remove rows from a table. A WHERE clause can be used to only remove some rows. If no WHERE condition is specified, all rows will be removed. After performing a DELETE operation you need to COMMIT or ROLLBACK the transaction to make the change permanent or to undo it. Note that this operation will cause all DELETE triggers on the table to fire. TRUNCATE: TRUNCATE removes all rows from a table. The operation cannot be rolled back and no triggers will be fired. As such, TRUCATE is faster and doesn't use as much undo space as a DELETE.

19 DROP: The DROP command removes a table from the database. All the tables' rows, indexes and privileges will also be removed. No DML triggers will be fired. The operation cannot be rolled back. 3. What are constraints and different types of it? Constraints enable the RDBMS enforce the integrity of the database automatically, without needing you to create triggers, rule or defaults. Types of constraints: PRIMARY KEY UNIQUE FOREIGN KEY CHECK NOT NULL A PRIMARY KEY constraint is a unique identifier for a row within a database table. Every table should have a primary key constraint to uniquely identify each row and only one primary key constraint can be created for each table. The primary key constraints are used to enforce entity integrity. A UNIQUE constraint enforces the uniqueness of the values in a set of columns, so no duplicate values are entered. The unique key constraints are used to enforce entity integrity as the primary key constraints. A FOREIGN KEY constraint prevents any actions that would destroy link between tables with the corresponding data values. A foreign key in one table points to a primary key in another table. Foreign keys prevent actions that would leave rows with foreign key values when there are no primary keys with that value. The foreign key constraints are used to enforce referential integrity. A CHECK constraint is used to limit the values that can be placed in a column. The check constraints are used to enforce domain integrity. A NOT NULL constraint enforces that the column will not accept null values. The not null constraints are used to enforce domain integrity, as the check constraints. You can create constraints when the table is created, as part of the table definition by using the CREATE TABLE statement.

20 4. What does UNION do? What is the difference between UNION and UNION ALL? UNION merges the contents of two structurally-compatible tables into a single combined table. The difference between UNION and UNION ALL is that UNION will omit duplicate records whereas UNION ALL will include duplicate records. It is important to note that the performance of UNION ALL will typically be better than UNION, since UNION requires the server to do the additional work of removing any duplicates. So, in cases where is is certain that there will not be any duplicates, or where having duplicates is not a problem, use of UNION ALL would be recommended for performance reasons. 5. Write a query to find nth highest salary. Select max(salary) from (Select emp.salary, ROW_NUMBER() over (order by emp.salary desc) rno From Employee emp) a Where rno = n; OR Select SALARY from (Select emp.salary, ROW_NUMBER() over (order by emp.salary desc) rno From Employee emp) a Where rno = n order by SALARY desc; Note: where rno =n, you can replace n by highest number you want to find. 6. Write a query to find duplicate records in table. Select <column_name>, count(*) From <Table_Name> Group by <column_name> Having count(*)>1; 7. Write a query to display unique records from table. Select distinct Table_Name.* From Table_Name; 8. What is difference between ROW_NUMBER(), RANK(), DENSE_RANK()? ROW_NUMBER():

21 This function will assign a unique id to each row returned from the query. RANK(): This function will assign a unique number to each distinct row, but it leaves a gap between the groups. DENSE_RANK(): This function is similar to Rank with only difference, this will not leave gaps between groups. DEPTNO RN R DR Where RN = ROW_NUMBER() R= RANK() DR= DENSE_RANK() 9. How to retrieve dropped table from database? Using Flashback command. Eg: FLASHBACK <TABLE_NAME>; NOTE: helpful for SQL Queries.