Data Engineering is a term one uses when working with data. The main process of converting the raw entity of data into useful information that can be used for various purposes is called Data Engineering. This involves the Data Engineer to work with the data by performing data collection and research on the same.
Data modeling is the simplification of complex software designs by breaking them up into simple diagrams that are easy to understand, and it does not require any prerequisites for the same. This provides numerous advantages as there is a simple visual representation between the data objects involved and the rules associated with them.
There are two schemas when one works with data modeling. They are:
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Hadoop is an open-source framework, which is used for data manipulation and data storage, as well as for running applications on units called clusters. Hadoop has been the gold standard of the day when it comes to working with and handling Big Data.
The main advantage is the easy provision of the huge amounts of space needed for data storage and a vast amount of processing power to handle limitless jobs and tasks concurrently.
There are many components involved when working with Hadoop, and some of them are as follows:
NameNode is one of the vital parts of HDFS. It is used as a way to store all the HDFS data and, at the same time, keep track of the files in all clusters as well.
However, you must know that the data is actually stored in the DataNodes and not in the NameNodes.
Hadoop streaming is one of the widely used utilities provided by Hadoop for users to easily create maps and perform reduction operations. Later, this can be submitted into a specific cluster for usage.
The following forms to be the vital foundation to Big Data:
Block is considered as a singular entity of data, which is the smallest factor. When Hadoop encounters a large file, it automatically slices the file into smaller chunks called blocks.
A block scanner is put into place to verify whether the loss-of-blocks created by Hadoop is put on the DataNode successfully or not.
The NameNode and the DataNode communicate via messages. There are two messages that are sent across the channel:
COSHH is the abbreviation for Classification and Optimization-based Scheduling for Heterogeneous Hadoop systems. As the name suggests, it provides scheduling at both the cluster and the application levels to directly have a positive impact on the completion time for jobs.
Star schema is also called the star join schema, which is one of the simple schemas in the concept of Data Warehousing. Its structure resembles a star that consists of fact tables and associated dimension tables. The star schema is widely used when working with large amounts of data.
The snowflake schema is a primary extension of the star schema with the presence of more dimensions. It is spanned across as the structure of a snowflake, hence the name. Data is structured here and split into more tables after normalization.
Following are the XML configuration files available in Hadoop:
FSCK is also known as the file system check, which is one of the important commands used in HDFS. It is primarily put to use when you have to check for problems and discrepancies in files.
Following are the three main methods involved with reducer:
Hadoop can be used in three different modes. They are:
Following are some of the steps involved in securing data in Hadoop:
Data Analytics helps the companies of today’s world in numerous ways. Following are the foundational concepts in which it helps:
A Data Engineer is responsible for a wide array of things. Following are some of the important ones:
Following are the important technologies that a Data Engineer must be proficient in:
Followed by this, a Data Engineer must also have good problem-solving skills and analytical thinking ability.
A Data Architect is a person who is responsible for managing the data that comes into the organization from a variety of sources. Data handling skills such as database technologies are a must-have skill of a Data Architect. The Data Architect is also concerned with how changes in the data will lead to major conflicts in the organization model.
Now, a Data Engineer is the person who is primarily responsible for helping the Data Architect with setting up and establishing the Data Warehousing pipeline and the architecture of enterprise data hubs.
The distance between nodes is the simple sum of the distances to the closest corresponding nodes. The getDistance() method is used to calculate these distances.
NameNode primarily consists of all of the metadata information for HDFS such as the namespace details and the individual block information.
Rack awareness is a concept in which the NameNode makes use of the DataNode to increase the incoming network traffic while concurrently performing reading or writing operation on the file, which is the closest to the rack in which the request was called from.
Heartbeat is one of the two ways the DataNode communicates with the NameNode. It is an important signal which is sent by the DataNode to the NameNode in a structured interval to show that it is still operational and working.
A context object is used in Hadoop, along with the mapper class, as a means of communication with the other parts of the system. System configuration details and jobs present in the constructor are obtained easily using the context object.
It is also used to send information to methods such as setup(), cleanup(), and map().
Hive is used to provide the user interface used to manage all the stored data in Hadoop. The data is mapped with HBase tables and worked on, as and when needed. Hive queries (similar to SQL queries) are executed to be converted into MapReduce jobs. This is done to keep the complexity under check when executing multiple jobs at once.
Metastore is used as a storage location for the schema and Hive tables. Data such as definitions, mappings, and other metadata can be stored in the metastore. This is later stored in an RDMS as and when needed.
Following are some of the components in Hive:
Yes, it is possible to create more than one table for a data file. In Hive, schemas are stored in the metastore. Therefore, it is very easy to obtain the result for the corresponding data.
Skewed tables are the tables in which values appear in a repeated manner. The more they repeat, the more the skewness.
Using Hive, a table can be classified as SKEWED while creating it. By doing this, the values will be written to different files first, and later, the other values that remain will go to a separate file.
Hive has the following collections/data types:
SerDe stands for Serialization and Deserialization in Hive. It is the operation that is involved when passing records through Hive tables.
The Deserializer takes a record and converts it into a Java object, which is understood by Hive.
Now, the Serializer takes this Java object and converts it into a format that is processable by HDFS. Later, HDFS takes over for the storage function.
Following are some of the table creation functions in Hive:
The role of the .hiverc file is initialization. Whenever you want to write code for Hive, you open up the CLI (command-line interface), and whenever the CLI is opened, this file is the first one to load. It contains the parameters that you initially set.
The *args function lets users define an ordered function for usage in the command line, and the **kwargs function is used to denote a set of arguments that are unordered and in line to be input to a function.
To see the structure of a database, the describe command can be used. The syntax is simple:
Yes, specific strings and corresponding substring operations can be performed in MySQL. The regex operator is used for this purpose.
When working with Data Warehousing, the primary focus goes on using aggregation functions, performing calculations, and selecting subsets in data for processing. With databases, the main use is related to data manipulation, deletion operations, and more. Speed and efficiency play a big role when working with either of these.
Interviewers look for candidates who are serious about advancing their career options by making use of additional tools like certifications. Certificates are strong proof that you have put in all efforts to learn new skills, master them, and put them into use at the best of your capacity. List the certifications, if you have any, and do talk about them in brief, explaining what all you learned from the program and how it’s been helpful to you so far.
This question is a frequent one. It is asked to understand if you have had any previous exposure to the environment and work in the same. Make sure to elaborate on the experience you have, with the tools you’ve used and the techniques you’ve implemented. This ensures to provide a complete picture to the interviewer.
Here, the interviewer is trying to see how well you can convince them regarding your proficiency in the subject, handling all the concepts needed to bring in large amounts of data, work with it, and help build a pipeline. It is always an added advantage to know the job description in detail, along with the compensation and the details of the company, thereby obtaining a complete understanding of what tools, software packages, and technologies are required to work in the role.
While answering this question, make sure to keep your explanation concise on how you would bring about a plan that works with the company setup and how you would implement the plan, ensuring that it works by first understanding the data infrastructure setup of the company, and you would also talk about how it can be made better or further improvised in the coming days with further iterations.
If you are interviewed for an intermediate-level role, this is a question that will always be asked. Begin your answer with a simple yes or no. It is alright if you have not worked with data modeling before, but make sure to explain whatever you know regarding data modeling to the interviewer in a concise and structured manner. It would be advantageous if you have made use of tools like Pentaho or Informatica for this purpose.
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