Discover 60 of the most popular data warehouse interview questions and answers. The advent of cloud technology and automation represents the data warehouse’s promising future.
Businesses value data more than any other resource, and they view their data as valuable. Essentially, the data warehouse sits at the center of the business intelligence system, analyzing and reporting data. With a solid understanding of data warehousing concepts, you can easily land a job as Big Data Architect, SQL Developer, Data Warehouse Developer, Data Analyst, and more.
A large volume of data is generated daily. Storing this data and ensuring that various departments can use it for analytical, reporting, and decision-making purposes is essential for reporting at various levels. Data warehousing is storing, collecting, and managing this data. This blog will discuss the top 66 data warehouse interview questions and answers you must learn in 2023.
A data warehouse is a central repository of all the data used by different parts of the organization. It is a repository of integrated information for queries and analysis and can be accessed later. When the data has been moved, it needs to be cleaned, formatted, summarized, and supplemented with data from many other sources. And this resulting data warehouse becomes the most dependable data source for report generation and analysis purposes.
Data mining is analyzing data from different perspectives, dimensions, and patterns and summarizing them into meaningful content. Data is often retrieved or queried from the database in its format. On the other hand, it can be defined as the method or process of turning raw data into useful information.
A data warehouse is for storing data from different transactional databases through the process of extraction, transformation, and loading. Data is stored periodically, and it stores a vast amount of data. Some use cases for data warehouses are product management and development, marketing, finance, banking, etc. It is used for improving operational efficiency and for MIS report generation and analysis purposes.
Whereas Data Mining is a process of discovering patterns in large datasets by using machine learning methodology, statistics, and database systems. Data is regularly analyzed here and is analyzed mainly on a sample of data. Some use cases are Market Analysis and management, identifying anomaly transactions, corporate analysis, risk management, etc. It is used for improving the business and making better decisions.
Data transformation is the process or method of changing data format, structure, or values.
|Types of data||Rational or non-relational or object-oriented||Large Volume with multiple data types|
|Data operations||Databases deal with transactional processing||Data warehouses deal with data modeling, analysis, and reporting|
|Dimension of data||Databases are two-dimensional because it deals with tables which are essentially 2d arrays.||In data warehouses, it can have multi-dimensional data they could be 3d 4d|
|Data design||Databases have ER-based and application-oriented database design||Data warehouses have star snowflake schema and subject-oriented database design|
|Size of data||Traditional databases, not extensive data databases, are small, usually in gigabytes.||Data warehouses are in the terabytes functionality for databases|
|Functionality||High availability and performance||It has flexibility and user autonomy because it will perform much analysis with the data warehouse.|
The primary reason for a data warehouse is for an organization to get an advantage over its competitors, which also helps the organization make smart decisions. Smarter decisions can only be taken if the executive responsibilities for making such decisions have data at their disposal.
Some of the major key characteristics of a data warehouse are listed below:
The difference between the 3 is as follows:
A database is typically structured with a defined schema so structured data can fit in a database; items are organized as tables with columns, columns indicate attributes and rows indicate an object or entity. It has to be structured and filled in here within all these rows and columns. Columns represent attributes, and rows refer to an object or entity. The database is transactional and generally not designed to perform data analytics. Some examples are Oracle, MySQL, SQL Server, PostgreSQL, MS SQL Server, MongoDB, Cassandra, etc. It is generally used to store and perform business functional or transactional data. You can also take an oracle SQL course to help you learn more.
A data warehouse exists on several databases and is used for business intelligence. The data warehouse gathers the data from all these databases and creates a layer to optimize data for analytics. It mainly stores processed, refined, highly modeled, highly standardized, and cleansed data.
A data lake is a centralized repository for structure and unstructured data storage. It can be used to store raw data without any structure schema, and there is no need to perform any ETL or transformation job. Any type of data can be stored here, like images, text, files, and videos, and even it can store machine learning model artifacts, real-time and analytics output, etc. Data retrieval processing can be done via export, so the schema is defined on reading. It mainly stores raw and unprocessed data. The main focus is to capture and store as much data as possible.
Data Mart lies between the data warehouse and Data Lake. It’s a subset of filtered and structured essential data of a specific domain or area for a specific business need.
A data model is simply a diagram that displays a set of tables and the relationship between them. This helps in understanding the purpose of the table as well as its dependency. A data model applies to any software development involving creating database objects to store and manipulate data, including transactional and data warehouse systems. The data model is being designed through three main stages: conceptual, logical, and physical data model.
A conceptual data model is a set of square shapes connected by a line. The square shape represents an entity, and the line represents a relationship between the entities. This is very high level and highly abstract, and key attributes should be here.
The logical data model expands the conceptual model by adding more detail and identifying its key and non-key attributes. Hence, key attributes or attributes define the uniqueness of that entity, such as in the time entity, it’s the date that’s a key attribute. It also considers the relationship type, whether one-to-one, one to many, or many to many.
The physical data model looks similar to a logical data model; however, there are significant changes. Here entities will be replaced by tables, and attributes will be referred to as columns. So tables and columns are words specific to a database. In contrast, entities and attributes are specific to a logical data model design, so a physical data model always refers to these as tables and columns. It should be database technology compatible.
Data Modelling is a very simple step of simplifying an entity here in the concept of data engineering. It will simplify complex software by simply breaking it up into diagrams and further breaking it into flow charts. Flowcharts are a simple representation of how a complex entity can be broken down into a simple diagram. This will give a visual representation, an easier understanding of the complex problem, and even better readability to a person who might not be proficient in that particular software usage.
Data modeling is generally defined as a framework for data to be used within information systems by supporting specific definitions and formats. It is a process used to define and analyze data requirements needed to support the business processes within the boundary of respective information systems in organizations. Therefore, the creation of data modeling involves experienced data modelers working closely with business stakeholders, as well as potential users of the information system.
Structure data is neat, has a known schema, and could fit in a fixed table. It uses the DBMS storage method, and Scaling schemas are complicated. Some of the following protocols are ODBS, SQL, ADO.NET, etc.
Whereas, Unstructured data has no schema or structure. It is mostly unmanaged, very easy to scale in runtime, and can store any type of data. Some of the followed protocols are XML,CSV, SMSM, SMTP, JASON etc.
An operational data store is used to store data from operational systems, and this data is typically used for reporting and analysis.
|Abbreviation||Online Transaction Processing||Online Analytical Processing|
|Used for||Day-to-day business transaction||Analyzed or reported purpose|
|Used by||End users, business users||Business Analyst, Decision Makers, Management level users|
|Data Insertion/ Change Frequency||Very frequent||Mostly fixed number of times through scheduled jobs|
|Mostly Used Statement||Select, Insert, Update, Delete||Select|
|Type of System or Source of data||Source system, Main source of data||Target system, data are transferred from OLTP through extraction, Transformation, and Loading process.|
|Data Volume||Less compared to OLAP||Very high|
|Processing speed or latency||Very fast||Depending on the amount of data, report generation SLA time can be a few seconds to a few hours.|
|Focus||More focus on ‘effective data storage’ and quick completion of the request. Hence generally, a limited number of indexes are used.||Focus on retrieval of data; hence more indexes are used.|
|Backup||A more frequent backup needs to be placed. Even runtime incremental backup is always recommended.||Time-to-time backup is less frequent, and no need for incremental runtime backup.|
The definition of Metadata is data about data. Metadata is the context that gives information a richer identity and forms the foundation for its relationship with other data. It can also be a helpful tool that saves time, keeps organized, and helps make the most of the files. Structural Metadata is information about how an object should be categorized to fit into a larger system with other objects. Structural Metadata establishes relationships with other files to be organized and used in many ways.
Administrative Metadata is information about the history of an object, who used to own it, and what can be done with it. Things like rights, licenses, and permissions. This information is helpful for people managing and taking care of an object.
One data point gains its full meaning only when it’s put in the right context. And the better-organized Metadata will reduce the searching time significantly.
|ER Modelling||Dimension Modelling|
|Used for OLTP Application design.Optimized for Select / Insert / Update / Delete||Used for OLAP Application design. Optimized for retrieving data and answering business queries.|
|Revolves around entities and their relationships to capture the process||Revolves around Dimensions for decision making, Doesn’t capture process|
|The unit of storage is a table.||Cubes are units of storage.|
|Contains normalized data.||Contains denormalized data|
A view is to access the data from its table that does not occupy space, and changes get affected in the corresponding tables. In contrast, in the materialized view, pre-calculated data persists, and it has physical data space occupation in the memory, and changes will not get affected in the corresponding tables. The material view concept came from database links, mainly used earlier to make a copy of remote data sets. Nowadays, it’s widely used for performance tuning.
The view always holds the real-time data, whereas Materialized view contains a snapshot of data that may not be real-time. Some methods are available to refresh the data in the Materialized view.
The data purging name is quite straightforward. It is the process involving methods that can erase data permanently from the storage. Several techniques and strategies can be used for data purging. The process of data forging often contrasts with data deletion, so they are not the same as deleting data is more temporarily while data purging permanently removes the data. This, in turn, frees up more storage and memory space which can be utilized for other purposes. The purging process allows us to archive data even if it is permanently removed from the primary source, giving us an option to recover that data in case we purge it. The deleting process also permanently removes the data but does not necessarily involve keeping a ba, and Itp generally involves insignificant amounts of data.
There are a couple of solutions available in the market. Some of the major solutions are:
Some of the major ETL tools are
A slowly changing dimension (SCD) is one that appropriately manages changes of dimension members over time. It applies when business entity value changes over time and in an ad-hoc manner.
There are six sorts of Slowly Changing Dimensions that are commonly used. They are as follows:
Type 0 – Dimension never changes here, dimension is fixed, and no changes are permissible.
Type 1 – No History Update record directly. There’s no record of historical values, only the current state. A kind 1 SCD always reflects the newest values, and the dimension table is overwritten when changes in source data are detected.
Type 2 – Row Versioning Track changes as version records which will be identified by the current flag & active dates, and other metadata. If the source system doesn’t store versions, the info warehouse load process usually detects changes and appropriately manages them during a dimension table.
Type 3 – Previous Value column Track change to a selected attribute, and add a column to point out the previous value, which is updated as further changes occur.
Type 4 – History Table shows the current value in the dimension table, and all changes are tracked and stored in a separate table.
Hybrid SCD – Hybrid SDC utilizes techniques from SCD Types 1, 2, and three to trace change.
Only types 0, 1, and a couple of are widely used, while the others are applied for specific requirements.
A factless fact is a fact table without any value, and such a table only contains keys from different dimension tables.
A fact table contains a business process’s measurements, metrics, or facts. It is located in the middle of a star schema or a snowflake schema, and dimension tables surround it.
Non-additive facts cannot sum up any of the dimensions in the fact table. If there is any change in the dimension, then the same facts can be useful.
A conformed fact is a table across multiple data marts and fact tables.
The core dimension is a Dimension table, which is dedicated to a single fact table or Data Mart.
Dimensional modeling is a set of guidelines to design database table structures for easier and faster data retrieval. It is a widely accepted technique. The benefits of using dimensional modeling are its simplicity and faster query performance. Dimension modeling elaborates logical and physical data models to further detail model data and data-related requirements. Dimensional models map the aspects of every process within the business.
Dimensional Modelling is a core design concept used by many data warehouse designers design data warehouses. During this design model, all the info is stored in two sorts of tables.
The fact table contains the facts or measurements of the business, and the dimension table contains the context of measurements by which the facts are calculated. Dimension modeling is a method of designing a data warehouse.
Types of Dimensional Modelling are listed below:
The basic difference is that E-R modeling has a logical and physical model while Dimensional modeling has only a physical model. E-R modeling is required to normalize the OLTP database design, whereas dimensional modeling is required to denormalize the ROLAP/MOLAP design.
A dimension table is a type of table that contains attributes of measurements stored in fact tables. It contains hierarchies, categories, and logic that can be used to traverse nodes.
In a data warehouse, a degenerate dimension is a dimension key in the fact table that does not have its dimension table. Degenerate dimensions commonly occur when the fact table’s grain is a single transaction (or transaction line).
One of the purposes of cluster analysis is to achieve scalability, so regardless of the quantity of data system will be able to analyze its ability to deal with different kinds of attributes, so no matter the data type, the attributes present in the data set can deal with its discovery of clusters with attribute shape high dimensionality which have multiple dimensions more than 2d to be precise ability to deal with noise, so any inconsistencies in the data to deal with that and interpretability.
The agglomerative hierarchical constraining method allows clusters to be read from bottom to top so that the program always reads from the sub-component first and then moves to the parent in an upward direction. In contrast, divisive hierarchical clustering uses a top-to-bottom approach in which the parent is visited first and then the child. The agglomerative hierarchical method consists of objects in which each object creates its clusters. These clusters are grouped to form a larger cluster. It is also the process of continuous merging until all the single clusters are merged into a complete big cluster that will consist of the objects of the chart clusters; however, in divisive clustering, the parent cluster is divided into smaller clusters. It keeps on dividing until each cluster has a singular object to represent.
ODS is a database that integrates data from multiple sources for additional data operations. The full form of ODS is the operational data source, unlike the master data source, where the data is not sent back to the operational systems. It may be passed for further operations and to the data warehouse for reporting. In ODS, data can be scrubbed, resolved for redundancy, and checked for compliance with the corresponding business rules, so whatever data is filtered out to see if there is some data redundancy. It is checked and shows whether the data complies with the organization’s business rules.
This data can be used for integrating disparate data from multiple sources so that business operations analysis and reporting can be carried out. This is where most of the data used in the current operation are housed before it’s transferred to the data warehouse for the longer term and storage and archiving.
For simple queries on small amounts of data, such as finding the status of a customer order, it is easier to find the details from ODS rather than Data warehousing as it does not make sense to search a particular customer order status on a larger dataset which will be more costly to fetch the single records. But for analyses like sentimental analysis, prediction, and anomaly detection where data warehousing will perform the role to play with its large data volumes.
ODS is similar to short-term memory, where it only stores very recent information. On the contrary, the data warehouse is more like a long-term memory storing relatively permanent information because a data warehouse is created permanently.
A fact table is usually designed at a low level of granularity. This means we must find the lowest amount of information stored in a fact table. For example, employee performance is a very high level of granularity. In contrast, employee performance daily and employee performance weekly can be considered low levels of granularity because they are much more frequently recorded data. The granularity is the lowest level of information stored in the fact table; the depth of the data level is known as granularity in the date dimension.
The level could be a year, month, quarter, period, week, and day of granularity, so the day is the lowest, and the year is the highest. The process consists of the following two steps determining the dimensions to be included and the location to find the hierarchy of each dimension of that information. The above factors of determination will be resent as per the requirements.
These are two philosophies that we’ve in data warehousing. Within the Kimball philosophy, data warehousing is viewed as a constituency of knowledge mods, so data mods are focused on delivering business objectives for departments in a corporation. Therefore the data warehouse may be a confirmed dimension of the info mods; hence a unified view of the enterprise is often obtained from the dimension modeling on a departmental area level.
Within the Inmon philosophy, we will create a knowledge warehouse on a topic-by-discipline basis; hence, the information warehouse can start with the in-web store’s information. The subject areas are often added to the info warehouse as their need arises point of sale, or pos data are often added later if management decides it’s required. We first accompany data marts if we check it out algorithmically within the Kimball philosophy. We combine it, and we get our data warehouse, while with Inmon philosophy, we create our data warehouse and then create our data marts.
Both differ within the concept of building the info Warehouse. – Kimball views Data Warehousing as a constituency of knowledge marts. Data marts are focused on delivering business objectives for departments in a corporation, and therefore the Data Warehouse may be a conformed dimension of the info Marts. Hence, a unified view of the enterprise is often obtained from the dimension modeling on a departmental area level. – Inmon explains creating a knowledge Warehouse on a subject-by-subject area basis. Hence, the event of the info Warehouse can start with data from the web store. Other subject areas are often added to the info Warehouse as their needs arise. Point-of-sale (POS) data is often added later if management decides it’s necessary.
ETL stands for extraction transformation and loading, so three phases are involved in it – the primary is the staging layer. The info integration layer and the last layer is the access layer. So these are the three layers involved in the three specific phases within the ETL cycle, so the staging layer is used for the info extraction from various source data structures.
Within the data integration layer, data from the staging layer is transformed and transferred to the info base using the mixing layer. The data is arranged in hierarchical groups often mentioned as dimensions facts or aggregates during a data warehousing system; the mixture of facts and dimension tables is called a schema, so basically, within the data integration layer, once the info is loaded and data extracted and transformed within the staging layer and eventually the access layer where the info is accessed and may be loaded for further analytics.
The idea behind OLAP was to pre-compute all calculations needed for reporting. Generally, calculations are done through a scheduled batch job processing at non-business hours when the database server is normally idle. The calculated fields are stored in a special database called an OLAP Cube.
An OLAP Cube doesn’t need to loop through any transactions because all the calculations are pre-calculated, providing instant access.
An OLAP Cube may be a snapshot of knowledge at a selected point in time, perhaps at the top of a selected day, week, month, or year.
You’ll refresh the Cube at any time using the present values within the source tables.
With very large data sets, it could take an appreciable amount of your time for Excel to reconstruct the Cube.
But the method appears instantaneous with the info sets we’ve been using (just a few thousand rows).
Chameleon may be a methodology that may be a hierarchical clustering algorithm that overcomes the restrictions of the prevailing models and methods in data warehousing. This method operates on the sparse graph having nodes representing data items and edges representing the weights of the info items. This representation allows large data sets to be created and operated successfully. The tactic finds the clusters utilized in the info set using the two-phase algorithm. The primary phase consists of graph partitioning that permits the clustering of the info items into a larger number of sub-clusters; the second phase, on the opposite hand, uses an agglomerative hierarchical clustering algorithm to look for the clusters that are genuine and may be combined alongside the sub-clusters that are produced.
A virtual data warehouse provides a collective view of the finished data. A virtual data warehouse has no historical data and is often considered a logical data model of the given Metadata. Virtual data warehousing is the de facto data system strategy for supporting analytical decisions. It’s one of the simplest ways of translating data and presenting it within the form decision-makers will employ. It provides a semantic map that allows the top user viewing because the data is virtualized.
An active data warehouse represents a single state of a business. Active data warehousing considers the analytical perspectives of customers and suppliers and helps show the updated data through reports. This is the most common form of data warehousing used for large businesses, specifically those that deal in the e-commerce or commerce industry. A form of repository of captured transactional data is known as active data warehousing.
Using this concept, trends and patterns are found to be used for future decision-making. Based on the analytical results from the data warehouse, it can perform other business decisions active data warehouse as a feature that can integrate the data changes. At the same time, scheduled cycles refresh enterprises utilize an active data warehouse and draw the company’s image in a very statistical manner. So everything is essentially a combination of all the data that is present in various data sources. Combine it all and then perform analytics to get insights for further business decisions.
Snapshots are pretty common in software, especially in databases, so essentially, it is what the name suggests. Snapshot refers to the complete visualization of data at the time of extraction. It occupies less space and can be used to back up and restore data quickly, so essentially, it snapshots a data warehouse when anyone wants to create a backup. So using the data warehouse catalog, It’s creating a report, and the report will be generated as shown as soon as the session is disconnected from the data warehouse.
XMLA is XML for analysis, and it is a SOAP-based XML protocol that can be used and considered as a standard for accessing data in the OLAP method, data mining, or data sources on the internet. The simple object access protocol XMLA uses to discover and execute methods that fetch information from the internet. In contrast, the execution allows the application to execute against the data sources in XMLA. XMLA is a standard methodology for accessing data in analytical systems such as OLAP. It is based on XML soap and HTTP XMLA specifies MDXML as a query language in XMLA 1.1 version. The only construct is the MDXML in an MDX statement enclosed in the tag.
A Junk Dimension is a dimension table consisting of attributes that do not belong in the fact table or any other existing dimension tables. The characteristics of these attributes are usually text or various flags, e.g., non-generic comments or very simple yes/no or true/false indicators. These attributes typically remain when all the apparent dimensions within the business process are identified. Thus the designer is faced with the challenge of where to place these attributes that don’t belong within the other dimensions.
In some scenarios, data might not be appropriately stored within the schema. The info or attributes are often stored during a junk dimension; the character of the junk during this particular dimension is typically Boolean or flag values. A single dimension is formed by lumping a small number of dimensions, and this is called a junk dimension adjunct dimension has unrelated attributes. The process of grouping these random flags and text attributes in a dimension by transmitting them to a distinguished sub-dimension is related to the junk dimension, so essentially, any data that need not be stored in the data warehouse because it is unnecessary is stored in the junk dimension.
SCDs stand for slowly changing dimensions, and it is a dimension where data changes do not happen frequently or regularly. There are three types of SCDs the first is SCD1, a record used to replace the original. Even when only one record exists within the database, the present data will be replaced, and the new data will take its place.
SCD2 is the new record file that is added to the dimension table. The record exists in the database with the current and previous data stored in the audit or history.
SCD3 uses the original data that is modified to the new data. This consists of two records, one that exists in the database and the other that will replace the old database record with this new information.
Multi-dimensional OLAP, also known as MOLAP, is faster than relational OLAP for the following reasons in MOLAP.
The data is stored in a multi-dimensional queue; the storage is not in the relational database but proprietary formats. MOLAP stores all the possible combinations of data in a multidimensional array.
Hybrid SCDs are combinations of both SCD1 and SCD2. It may happen that in a table, some columns are important and need to track changes for them that are captured by the historical data for them. In some columns, even if the data changes, that does not need to bother. For such tables, hybrid SCDs are implemented wherein some columns are of type 1, and some are of type 2. So basically, a blanket rule is not applied to the entire table rather than customized on which particular columns where a particular rule needs to be applied.
We can develop the action servlets and the action form servlets, and other circuit classes in the action form class. You can develop a validated method that can return action errors object in this method. One can also write the validation code if this method returns null or action errors with the size of zero. The web container will call execute as part of the action class, and it will call the execute method if it returns a size greater than zero. It will rather execute the JSP servlet, or the HTML file as the value for the input attribute is part of the attribute in the struts-config XML file.
VLDB stands for a very large database, and it is a database that contains a particularly sizable amount of tuples or rows or occupies a particularly large physical file system storage. VLDB database sizes are normally in Terabytes only.
Time dimensions are usually loaded by a program that loops through all possible dates appearing within the data, and it’s a common place for 100 years to be represented during a time dimension with one row per day.
Conform dimensions can be used across multiple data marks in combination with multiple fact tables. A conformed dimension is a dimension that has the same meaning and contents; when being referred to from different fact tables, it can refer to multiple tables in multiple data marts within the same organization itself.
ETL test is performed in five stages which are the following the identification of data sources and requirements; first, you will identify which data sources you want for your data warehouse and what are the requirement of the data warehouse, and the analytical requirements that your organization needs the acquisition of data naturally after identifying the data source you will acquire that data implementing business logic and dimensional modeling on that data building and publishing that data and the reports that you will create out of the analytics that you perform.
A slice operation is the filtration process in a data warehouse. It selects a specific dimension from a given cube and provides a new sub-cube in the slice operation. Only a single dimension is used, so, out of a multi-dimensional data warehouse, if it needs a particular dimension that needs further analytics or processing, it will use the slice operation in that data warehouse.
There are 7 Steps to Data Warehousing:
Data cleaning is the process that removes data that doesn’t belong in your dataset. Data transformation is how data from one format or structure converts into another. Transformation processes can also be mentioned as data wrangling or data mugging, transforming, and mapping data from one “raw” data form into another for warehousing and analysis. This text focuses on the processes of cleaning that data.
Normalization is a multi-step process that puts data into tabular form, removing duplicated data from the relation tables.
Normalization helps in reducing data redundancy, and thus it saves physical database spaces and has minimal write operation cost.
Denormalization is employed to access the info from a higher or lower regular database, and it creates redundancy and stores multiple copies of the same data in different tables.
Denormalization adds required redundant terms into the tables to avoid using complex joins and many other complex operations. Denormalization doesn’t mean that normalization won’t be done, but the denormalization process takes place after the normalization process.
An Extent is a fixed number of contiguous data blocks as per configuration. It is obtained during a single allocation and used to store a specific type of information.
An Index is associated with a database table for quick data search or filter operation retrieval. An index can consist of one or more columns associated with it. Different types of indexes are available in databases, like Unique Key indexes, primary key indexes, Bitmap indexes, and B-Tree indexes. Indexes also hold separate tablespace for storing the preferences of data. Indexes are not recommended where insert, update and delete operations frequently occur rather than a select statement.
A source qualifier represents the rows the Server reads when it executes a session. Source qualifier transformation needs to be connected for the addition of a relational or a flat file source definition to a mapping.
ETL Pipeline refers to a group of processes to extract the info from one system, transform it, and cargo it into some database or data warehouse. They are built for data warehousing applications that incorporate enterprise data warehouses and subject-specific data marts. They are also used for data migration solutions. Data warehouse/ business intelligence engineers build ETL pipelines.
Data Pipeline refers to any set of process elements that move data from one system to a different one. Data Pipeline is often built for an application that uses data to bring value. It is often used to integrate the info across the applications, build info-driven web products, and complete data mining activities. Data engineers build the data pipeline.
A fact may be a central component of a multi-dimensional model that contains the measures to be analyzed. Facts are related to dimensions.
Types of facts are:
We are at the end of the blog on the top 66 data warehouse interview questions. We hope you found this helpful and are now better equipped to attend your upcoming interview sessions. If you wish to learn more about such concepts, join Great Learning’s PGP Data Science and Business Analytics Course to upskill today. Great Learning also offers mentor support, interview preparation, and live sessions with industry experts!
The 12-week Applied Data Science Program has a curriculum carefully crafted by MIT faculty to provide the skills, knowledge, and confidence you need to flourish in the Industry. The program not only focuses on Recommendation Systems but also on other business-relevant technologies, such as Machine Learning, Deep Learning, and more. The top-rated data science program prepares you to be an important part of data science efforts at any organization.
Also, Read the Top 25 Common Interview Questions
What are the 5 components of a data warehouse?
There are primarily 5 components of Data Warehouse Architecture:
2) ETL Tools
3) Meta Data
4) Query Tools
What are the basic 4 features of data warehousing?
The primary 4 features of data warehousing are as follows:
44) Persistent & non-volatile
What are the three main types of data warehouses?
The three main types of Data warehouses are Enterprise Data Warehouse (EDW), Operational Data Store, and Data Mart.
What is ETL in data warehousing?
ETL, short for extract, transform, and load, is ideally a data integration system known to bring together data from several data sources into a reliable data store that is then loaded into a data warehouse or other destination point.
What are OLAP and OLTP?
Although both terms may sound similar, they have some distinct qualities. Online transaction processing (OLTP) is the real-time capture, archiving, and processing of data from transactions. Complex queries are used in online analytical processing (OLAP) to examine past aggregated data from OLTP systems.
Original article source at: https://www.mygreatlearning.com
Data warehouse interview questions listed in this article will be helpful for those who are in the career of data warehouse and business intelligence. With the advent of machine learning, a large volume of data needs to be analyzed to get the insights and implement results faster. Those days are gone when the data processing steps were data storage, assimilation, fetching, and processing. But as the volume of data increases, such data needs to be processed and show instant results.
All the businesses such as healthcare, BFSI, utilities, and many government organizations are changing to the data warehouse. As a result of this, more professionals having expertise in the data warehouse get hired so that they can analyze the large volumes of data and provide relevant insights. Thus, data warehouse interview questions become pertinent to easily crack the interviews and to get important knowledge.
If you are passionate about handling massive data and managing databases, then a data warehouse is a great career option for you. In this article, you will get the data warehouse interview questions that can help you with your next interview preparation. The questions are from basic to expert level, so both fresher and experienced professionals will get benefited from these data warehouse interview questions.
#data science #data warehouse #data warehouse interview #data warehouse interview questions #data warehouse interview questions and answers
Android Interview Questions and Answers from Beginner to Advanced level
DataFlair is committed to provide you all the resources to make you an android professional. We started with android tutorials along with practicals, then we published Real-time android projects along with source code. Now, we come up with frequently asked android interview questions, which will help you in showing expertise in your next interview.
Android – one of the hottest technologies, which is having a bright future. Get ready to crack your next interview with the following android interview questions. These interview questions start with basic and cover deep concepts along with advanced topics.
1. What is Android?
Android is an open-source mobile operating system that is based on the modified versions of Linux kernel. Though it was mainly designed for smartphones, now it is being used for Tablets, Televisions, Smartwatches, and other Android wearables.
2. Who is the inventor of Android Technology?
The inventors of Android Technology are- Andry Rubin, Nick Sears, and Rich Miner.
3. What is the latest version of Android?
The latest version of Android is Android 10.0, known as Android Q. The upcoming major Android release is Android 11, which is the 18th version of Android. [Note: Keep checking the versions, it is as of June 2020.]
4. How many Android versions can you recall right now?
Till now, there are 17 versions of Android, which have their names in alphabetical order. The 18th version of Android is also going to come later this year. The versions of Android are here:
5. Explain the Android Architecture with its components.
This is a popular android developer interview question
Android Architecture consists of 5 components that are-
a. Linux Kernel: It is the foundation of the Android Architecture that resides at the lowest level. It provides the level of abstraction for hardware devices and upper layer components. Linux Kernel also provides various important hardware drivers that act as software interfaces for hardwares like camera, bluetooth, etc.
b. Native Libraries: These are the libraries for Android that are written in C/C++. These libraries are useful to build many core services like ART and HAL. It provides support for core features.
c. Android Runtime: It is an Android Runtime Environment. Android Operating System uses it during the execution of the app. It performs the translation of the application bytecode into the native instructions. The runtime environment of the device then executes these native instructions.
d. Application Framework: Application Framework provides many java classes and interfaces for app development. And it also provides various high-level services. This complete Application framework makes use of Java.
e. Applications: This is the topmost layer of Android Architecture. It provides applications for the end-user, so they can use the android device and compute the tasks.
6. What are the services that the Application framework provides?
The Android application framework has the following key services-
a. Activity Manager: It uses testing and debugging methods.
b. Content provider: It provides the data from application to other layers.
c. Resource Manager: This provides users access to resources.
d. Notification Manager: This gives notification to the users regarding actions taking place in the background.
e. View System: It is the base class for widgets, and it is also responsible for event handling.
7. What are the important features of Linux Kernel?
The important features of the Linux Kernel are as follows:
a. Power Management: Linux Kernel does power management to enhance and improve the battery life of the device.
b. Memory Management: It is useful for the maximum utilization of the available memory of the device.
c. Device Management: It includes managing all the hardware device drivers. It maximizes the utilization of the available resources.
d. Security: It ensures that no application has any such permission that it affects any other application in order to maintain security.
e. Multi-tasking: Multi-tasking provides the users the ease of doing multiple tasks at the same time.
8. What are the building blocks of an Android Application?
This is a popular android interview question for freshers.
The main components of any Android application are- Activity, Services, Content Provider, and Broadcast Receiver. You can understand them as follows:
a. Activity- It is a class that acts as the entry point representing a single screen to the user. It is like a window to show the user interface.
b. Services- Services are the longest-running component that runs in the background.
c. Content Provider- The content provider is an essential component that allows apps to share data between themselves.
d. Broadcast receivers- Broadcast receiver is another most crucial application component. It helps the apps to receive and respond to broadcast messages from the system or some other application.
9. What are the important components of Android Application?
The Components of Android application are listed below:
10. What are the widgets?
Widgets are the variations of Broadcast receivers. They are an important part of home screen customization. They often display some data and also allow users to perform actions on them. Mostly they display the app icon on the screen.
11. Can you name some types of widgets?
Mentioned below are the types of widgets-
a. Informative Widgets: These widgets show some important information. Like, the clock widget or a weather widget.
b. Collective Widgets: They are the collection of some types of elements. For example, a music widget that lets us change, skip, or forward the song.
c. Control Widgets: These widgets help us control the actions within the application through it. Like an email widget that helps check the recent mails.
d. Hybrid Widgets: Hybrid widgets are those that consist of at least two or more types of widgets.
12. What are Intents?
Intents are an important part of Android Applications. They enable communication between components of the same application as well as separate applications. The Intent signals the Android system about a certain event that has occurred.
13. Explain the types of intents briefly?
Intent is of three types that are-
a. Implicit Intents: Implicit intents are those in which there is no description of the component name but only the action.
b. Explicit Intents: In explicit intents, the target component is present by declaring the name of the component.
c. Pending Intents: These are those intents that act as a shield over the Intent objects. It covers the intent objects and grants permission to the external app components to access them.
14. What is a View?
A view is an important building block that helps in designing the user interface of the application. It can be a rectangular box or a circular shape, for example, Text View, Edit Text, Buttons, etc. Views occupy a certain area of the screen, and it is also responsible for event handling. A view is the superclass of all the graphical user interface components.
15. What do you understand by View Group?
It is the subclass of the ViewClass. It gives an invisible container to hold layouts or views. You can understand view groups as special views that are capable of holding other views, that are Child View.
16. What do you understand about Shared Preferences?
It is a simple mechanism for data storage in Android. In this, there is no need to create files, and using APIs, it stores the data in XML files. It stores the data in the pair of key-values. SharedPreferences class lets the user save the values and retrieve them when required. Using SharedPreferences we can save primitive data like- boolean, float, integer, string and long.
17. What is a Notification?
A notification is just like a message that shows up outside the Application UI to provide reminders to the users. They remind the user about a message received, or some other timely information from the app.
18. Give names of Notification types.
There are three types of notifications namely-
a. Toast Notification- This notification is the one that fades away sometime after it pops up.
b. Status Notification- This notification stays till the user takes some action on it.
c. Dialog Notification- This notification is the result of an Active Activity.
19. What are fragments?
A fragment is a part of the complete user interface. These are present in Activity, and an activity can have one or more fragments at the same time. We can reuse a fragment in multiple activities as well.
20. What are the types of fragments?
There are three types of fragments that are: Single Fragment, List Fragment, Fragment Transactions.
21. What are Layout XML files?
Layout XML files contain the structure for the user interface of the application. The XML file also contains various different layouts and views, and they also specify various GUI components that are there in Activity or fragments.
22. What are Resources in Android Application?
The resources in Android Apps defines images, texts, strings, colors, etc. Everything in resources directory is referenced in the source code of the app so that we can use them.
23. Can you develop Android Apps with languages other than Java? If so, name some.
Yes, there are many languages that we can work with, for the development of Android Applications. To name some, I would say Java, Python, C, C++, Kotlin, C#, Corona/LUA.
24. What are the states of the Activity Lifecycle?
Activity lifecycle has the following four stages-
a. Running State: As soon as the activity starts, it is the first state.
b. Paused State: When some other activity starts without closing the previous one, the running activity turns into the Paused state.
c. Resume State: When the activity opens again after being in pause state, it comes into the Resume State.
d. Stopped State: When the user closes the application or stops using it, the activity goes to the Stopped state.
25. What are some methods of Activity?
The methods of Activity are as follows:
26. How can you launch an activity in Android?
We launch an activity using Intents. For this we need to use intent as follows:
27. What is the service lifecycle?
There are two states of a service that are-
a. Started State: This is when the service starts its execution. A Services come in start state only through the startService() method.
b. Bounded State: A service is in the bounded state when it calls the method bindService().
28. What are some methods of Services?
The methods of service are as follows-
29. What are the types of Broadcast?
Broadcasts are of two types that are-
a. Ordered Broadcast: Ordered broadcasts are Synchronous and work in a proper order. It decides the order by using the priority assigned to the broadcasts.
b. Normal Broadcast: These are asynchronous and unordered. They are more efficient as they run unorderly and all at once. But, they lack full utilization of the results.
30. What are useful impotent folders in Android?
The impotent folders in an Android application are-
31. What are the important files for Android Application when working on Android Studio?
This is an important android studio interview question
There are following three files that we need to work on for an application to work-
a. The AndroidManifest.xml file: It has all the information about the application.
b. The MainActivity.java file: It is the app file that actually gets converted to the dalvik executable and runs the application. It is written in java.
c. The Activity_main.xml file: It is the layout file that is available in the res/layout directory. It is another mostly used file while developing the application.
32. Which database do you use for Android Application development?
The database that we use for Android Applications is SQLite. It is because SQLite is lightweight and specially developed for Android Apps. SQLite works the same way as SQL using the same commands.
33. Tell us some features of Android OS.
The best features of Android include-
34. Why did you learn Android development?
Learning Android Studio is a good idea because of the following-
35. What are the different ways of storage supported in Android?
The various storage ways supported in Android are as follows:
36. What are layouts?
Layout is nothing but arrangements of elements on the device screen. These elements can be images, tests, videos, anything. They basically define the structure of the Android user interface to make it user friendly.
37. How many layout types are there?
The type of layouts used in Android Apps are as follows:
38. What is an APK?
An APK stands for Android Package that is a file format of Android Applications. Android OS uses this package for the distribution and installation of the Android Application.
39. What is an Android Manifest file?
The manifest file describes all the essential information about the project application for build tools, Android operating system, and google play. This file is a must for every Android project that we develop, and it is present in the root of the project source set.
#android tutorials #android basic interview questions #android basic questions #android developer interview questions #android interview question and answer #android interview questions #android interview questions for experienced #android interview questions for fresher
offers powerful features for the rapid development of deployment-ready applications. It is the most used and best java framework for the development of scalable microservices and web applications.
If you want to become a domain expert, you have come to the right place. We have curated some the most repeatedly asked spring boot interview questions and answers to help you ace the interview.
#full stack development #interview question answer #spring boot interview questions answer #top spring boot interview questions #top 10 critical spring boot interview questions #answers
In today’s market reliable data is worth its weight in gold, and having a single source of truth for business-related queries is a must-have for organizations of all sizes. For decades companies have turned to data warehouses to consolidate operational and transactional information, but many existing data warehouses are no longer able to keep up with the data demands of the current business climate. They are hard to scale, inflexible, and simply incapable of handling the large volumes of data and increasingly complex queries.
These days organizations need a faster, more efficient, and modern data warehouse that is robust enough to handle large amounts of data and multiple users while simultaneously delivering real-time query results. And that is where hybrid cloud comes in. As increasing volumes of data are being generated and stored in the cloud, enterprises are rethinking their strategies for data warehousing and analytics. Hybrid cloud data warehouses allow you to utilize existing resources and architectures while streamlining your data and cloud goals.
#cloud #data analytics #business intelligence #hybrid cloud #data warehouse #data storage #data management solutions #master data management #data warehouse architecture #data warehouses
If you accumulate data on which you base your decision-making as an organization, you should probably think about your data architecture and possible best practices.
If you accumulate data on which you base your decision-making as an organization, you most probably need to think about your data architecture and consider possible best practices. Gaining a competitive edge, remaining customer-centric to the greatest extent possible, and streamlining processes to get on-the-button outcomes can all be traced back to an organization’s capacity to build a future-ready data architecture.
In what follows, we offer a short overview of the overarching capabilities of data architecture. These include user-centricity, elasticity, robustness, and the capacity to ensure the seamless flow of data at all times. Added to these are automation enablement, plus security and data governance considerations. These points from our checklist for what we perceive to be an anticipatory analytics ecosystem.
#big data #data science #big data analytics #data analysis #data architecture #data transformation #data platform #data strategy #cloud data platform #data acquisition