If you're looking for Data Architect Interview Questions for Experienced or Freshers, you are at right place. There are a lot of opportunities for many reputed companies in the world. According to research Data Architect Market expected to reach $128.21 Billion with 36.5% CAGR forecast to 2022. So, You still have an opportunity to move ahead in your career in Data Architecture. Mindmajix offers Advanced Data Architect Interview Questions 2019 that helps you in cracking your interview & acquire dream career as Data Architect.
|Data Architect||Data Engineer||Data Analyst||Data Scientist|
|Data Warehouse Solutions||Extractions, Transformation and Load(ETL)||Data Collection and Processing||Data Cleansing and Processing|
|Extractions, Transformation and Load(ETL)||Installing Data Warehousing Solutions||Programming||Predictive modeling|
|Data Architecture Development||Data Modeling||Machine Learning||Machine Learning|
|Data Modeling||Data Architecture Construction And Development||Data munging||Identifying Questions|
|Database Architecture Testing||Data Visualization||Running Queries|
|Applying Statistical Analysis||Applying Statistical Analysis|
|Correlating Disparate Data|
|Story Telling and Visualization|
The individual who is into data architect role is a person who can be considered as a data architecture practitioner. So when it comes to data architecture it includes the following stages:
All of these activities are carried out with the organization's data architecture.
With their help and skill set, the organization can take a constructive decision of how the data is stored, how the data is consumed and how the data is integrated into different IT systems. In a sense, this process is closely aligned with business architecture, because they should be aware of this process so that the security policies are also taken into consideration.
The fundamental skills of a Data Architect are as follows:
1. The individual should possess knowledge about data modeling in detail
2. Physical data modeling concepts
3. Should be familiar with ETL process
4. Should be familiar with Data warehousing concepts
5. Hands-on experience with data warehouse tools and different software
6. Should have experience in terms of developing data strategies
7. Build data policies and plans for executions
A data block is nothing but a logical space where the Oracle database data is stored.
A data file is nothing but a file where all the data is available. For every Oracle database, we will be having one or more data files associated.
A cluster analysis is defined as a process where an object is defined without giving any label to it. It uses statistical data analysis technique and processes the data mining job. Using cluster analysis, an iterative process of knowledge discovery is processed in the form of trails.
The purpose of cluster analysis:
1. It is scalable
2. It can deal with different set of attributes
3. High dimensionality
A virtual data warehouse provides a view of completed data. Within Virtual data warehousing, it doesn’t have any historical data and it can be considered as a logical data model which has the metadata. A virtual data warehouse is a perfect information system where it acts as an appropriate analytical decision-making system.
It is one of the best ways of portraying raw data in the form of meaningful data for executive users which makes business sense and at the same time it provides suggestions at the time of decision making.
As the name itself implies, the snapshot is nothing but a set of complete data visualization when a data extraction is executed. The best part is that it uses less space and it can be easily used to take backup and also the data can be restored quickly from a snapshot.
XMLA is nothing but XML for analysis purposes. This is considered as a standard for access of data in OLAP. XMLA actually uses discover and execute methods. So Discover method actually is used to fetch the information from the internet and execute method is used for the applications to execute against all the data sources that are available.
The main difference between view and materialized view is as follows:
1. Data representation is provided by view where the data is accessed from its table.
2. View has a logical structure which does not occupy space
3. All the changes are affected in corresponding tables.
1. Within materialized view, pre-calculated data is available
2. The materialized view has a physical structure which does occupy space
3. All the changes are not reflected in the corresponding tables.
A junk dimension is nothing but a dimension where a certain type of data is stored which is not appropriate to store in the schema. The nature of the junk dimension is usually a Boolean has flag values.
A single dimension is formed by a group of small dimensions got together. This can be considered as junk dimension.
The data warehouse architecture is a three-tier architecture. The following is the three-tier architecture:
1. Bottom Tier
2. Middle Tier
3. Upper Tier
It is nothing but a repository of integrating data which is extracted from different data sources.
An integrity constraint is nothing but a specific requirement that the data in the database has to meet. It is nothing but a business rule for a particular column in a table. In the data warehouse concept, they are 5 integrity constraints.
The following are the integrity constraints:
2. Unique key
3. Primary key
4. Foreign key
The primary idea of keeping the standards high on compliance for data standards is because it will help to reduce the data redundancy and helps the team to have a quality data. As this information is actually carried out or used throughout the organization.
There are three different kinds of data models that are available and they are as follows:
Conceptual data model:
As the name itself implies that this data model depicts the high-level design of the available physical data.
Logical data model:
Within the logical model, the entity names, entity relationships, attributes, primary keys and foreign keys will show up.
Physical data model:
Based on this data model, the view will give out more information and showcases how the model is implemented in the database. All the primary keys, foreign keys, tables names and column names will be showing up.
In short, dimensions are nothing but which represents qualitative data. For example data like a plan, product, class are all considered as dimensions.
The attribute is nothing but a subset of a dimension. Within a dimension table, we will have attributes. The attributes can be textual or descriptive. For example, product name and product category are nothing but an attribute of product dimensions.
If it is an OLAP route then it is going to be a snowflake schema design.
The following are the prerequisites for an individual to start his career in Data Architect.
1. A bachelor's degree is essential and preferably in computer science background
2. No predefined certifications are necessary, but it is always good to have few certifications related to the field because few of the companies might expect. It is advisable to go through CDMA (Certified 3. Data Management Professional)
4. Should have at least 3-8 years of IT experience.
5. Should be creative, innovative and good at problem-solving.
6. Has good programming knowledge and data modeling concepts
7. Should be well versed with the tools like SOA, ETL, ERP, XML etc
No, not at all. The responsibilities of data architect are completely different from that of data administrator. For example:
Data architect works on with data modeling and designs the database design in a robust manner where the users will be able to extract the information easily. When it comes to data administrators, they are responsible for having the databases run efficiently and effectively.
No, data architect and data scientist roles are two different roles in an organization. The following are few activities that data architect is involved :
1. Data warehousing solutions
2. ETL activities
3. Data Architecture development activities
4. Data modelling
5. The following are few activities that data scientist is involved in:
6. Data cleansing and processing
7. Predictive modelling
8. Machine learning
9. Statistical analysis applied
10. Data visualization
The three different types of measures are available, they are as follows:
1. Non-additive measures
2. Semi-additive measures
3. Additive measures
The common mistakes that are encountered during data modeling activities are listed below:
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