Do you have a technical skill? And are you having the capability to become a Netezza developer? Then you are to the right place to learn and gain professional experience, HKR has come up with a list of Netezza interview questions and Answers article which is designed by the SME expert team. As per the Gartner report, there is a huge buzz created for Database administrators so you can expect a huge salary offer. Why Netezza is so popular? The answer is that here user can fast access the consumer data, and also offers incredible analytical solutions. So what you are waiting for, let’s begin learning Netezza concepts:
Ans: Netezza is a SQL based platform developed by one of the top companies in the world “IBM” Pvt. Ltd. IBM Netezza is a subsidiary of American technology. This technology offers high-performance data ware appliances and advanced analytical applications. The important functions included are data appliance warehousing, business intelligence, analytic predictions, and business planning. One important thing is that Netezza supports only one data type that is SQL at a time.
Ans: Below are the important advantages of using the Netezza application such as;
Ans: The environments are as follows;
Ans: The different states of Netezza are as follows;
Ans: The following are various types of data warehouse appliance available they are;
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Ans: When the application starts reading the data from the desk, the FGPA (Field programmable GateWay) will filters unwanted data sets. This FRGA process also helps in the data elimination process, IO bottlenecks, and frees up the components such as memory, CPU, and network management.
Ans: In Netezza, the zone maps are generated and refreshed in SPU with the help of the following mechanisms such as;
Ans: The following are the list of enforced and non enforced constraints in Netezza.
Ans: Netezza uses Field Programmable Gate Array (FPGA) to filter out unwanted data when reading the data from the disk. This removes IO bottlenecks and frees up CPU, memory, and network components.
Ans: A snippet is nothing but a small unit of work that will be carried out by SPU.
Ans: Zone maps are the smallest unit disk allocation located on the SPU. Zone maps are considered as internal mapping structures that will extend the internal ordering data and this eliminates the data extents, no need for any scanning. The purpose of using Zone mapping is to offer transparency and avoids unreferenced rows scanning. In Netezza, the zone maps are generated for each data column this contains minimum/maximum values for the extents.
Ans: Netezza gathers stats about the tables in the database through 'generate statics'. The stats include null values, duplicate values, maximum values, minimum values, etc. There are two ways to generate statistics.
Ans: Materialized views reduce the width of data (number of columns). It creates a thin version of the base table, such that it only contains the frequently queried columns. It will have the same distribution key as the base table.
Ans: Here are some of the best practices to follow when working with material views.
Ans: Below are the few limits of materialized views;
Ans: The two partitioning methods are used;
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Ans: No, Netezza does not cache everything. It will only cache the table schema and other database objects. The SPUs hold the actual data of the tables.
Ans: Netezza supports the following join types,
Ans: Netezza supports 3 data loading formats for loading the data from external sources.
Ans: If the same record is updated concurrently, Netezza will roll back the affected transaction that was performed recently on the same record. Netezza locks the table by using serializable transaction isolation to ensure no dirty reads are being performed.
Ans: The cumulative sum or running sum can be calculated for queries by using the Netezza analytic functions.
Ans: Netezza does not have a primary key enforced. So, we can insert duplicate rows into the Netezza tables.
Ans: We have multiple ways to identify and delete duplicate rows from a table,
Ans: The best way to redistribute a table is to create a new table using CTAS (Create table AS) and load the data at the same time. Alternatively, we can also change the distribution property in the /nz/data/postgresql.conf configuration file and restart the database.
Ans: If two tables share the same distribution key, then those tables are called collocated tables. When two collocated tables are joined together, then it is called a collocated join.
Ans: The nzload is a utility or command that is used to load bulk data from a file into a table. The nzload can upload data from the localhost or a remote client.
Ans: There are four ways to upload data into a Netezza table,
Ans: Netezza allows only one transaction to perform on a table at a time. The transactions are maintained through two slots - create xid and delete xid. Each row or record will contain these two slots.
Ans: No, we cannot update distribution columns in the Netezza tables. The tables are distributed across several nodes using the distribution column. If you try to update the distribution columns, it will throw an error and lock the entire table.
Ans: The data loaded to Netezza will be stored in the Snippet Processing Unit (SPU). Each SPU has a dedicated hard drive.
Ans: Netezza stores system catalog information in the Netezza hosts. Each time a table gets created in Netezza, table definition gets stored in hosts. The actual rows or records of the table gets stored in the Netezza disks.
Ans: Clustering is the process of grouping similar items. Netezza consists of 2 SMP hosts that are part of a cluster - one for active host and the other for the passive host.
Ans: In Netezza, we can't change the extent and page sizes, they are fixed by default. The extent size is 3 MB, and the page size is 128 KB.
Ans: The following are the list of enforced and non enforced constraints in Netezza.
Ans: Netezza uses Field Programmable Gate Array (FPGA) to filter out unwanted data when reading the data from the disk. This removes IO bottlenecks and frees up CPU, memory, and network components.
Ans: Netezza gathers stats about the tables in the database through 'generate statics'. The stats include null values, duplicate values, maximum values, minimum values, etc. There are two ways to generate statistics.
Ans: Materialized views reduce the width of data (number of columns). It creates a thin version of the base table, such that it only contains the frequently queried columns. It will have the same distribution key as the base table.
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