Looking at the number of updating new data science job in their LinkedIn profile, it seems the lockdown has not affected the hiring spree of data science professionals. That’s a great sign for big data aspirants, but you need to understand that hands-on experience and a sound preparation for the interview are the keys to success. A well-prepared interview session will set you apart from the lot.
What is big data?
A large amount of raw data generated from different digital sources like social media platforms, internet, and smart phones. Big Data Analytics is the use of proper tools to transform, cleanse, profile and aggregate the data for informed Business decisions.
We are listing some of the most frequently asked Big Data interview questions. Though every Big Data Interview Questions can different all the time or could be based on the scope of a job, we can help you out with the top Big Data Interview Questions along with their answers. We hope this article will take you a step closer to your successful career in Big Data.
What do you know about the term “Big Data”?
The term Big Data is used for complex and large datasets. As a relational database cannot handle big data, businesses use special methods and tools to perform data analysis on large unstructured data sets. Big data enables businesses to understand their business better and helps them take decisions that impacts their business for good.
What are V’s of Big Data?
There are five V’s of Big data:
- Volume – It represents the volume of data that is being accumulated at a high rate
- Velocity – It represents the rate at which at which data grows. Social media is the largest contributor to this.
- Variety – It represents the different data types and includes formats not limited to text, audios, videos, etc.
- Veracity – It represents the uncertainty of available and incoming data and arises due to the inconsistency in data feeds.
- Value – It represents the turning data into value. By turning accessed big data into value, businesses generate high returns on investments.
How are Hadoop and Big Data related?
Hadoop is an open-source framework that facilitates scalable storing, processing, and analyzing complex unstructured data sets to deriver business impacting insights and intelligence. Hadoop is widely used by businesses as their primary big data analytics tool. Hadoop takes major part with its capabilities of:
- Data collection
How are NAS (Network-attached storage) and HDFS different?
The main differences between NAS (Network-attached storage) and HDFS are –
|HDFS runs on a cluster of machines||NAS runs on an individual machine|
|Data redundancy is a common issue in HDFS||Data redundancy is much less|
|Data is stored as data blocks in local drives||Data is stored in dedicated hardware.|
What are the steps followed while deploying a Big Data solution?
There are primarily three steps that we follow while deploying a Big Data Solution. They are –
- Data Ingestion
- The first step for deploying a big data solution is the extraction of data from various sources. The data source may be a CRM like Salesforce, Enterprise Resource Planning System like SAP, RDBMS like MySQL, or any other log files, documents, social media feeds, etc. The data can be ingested either through batch jobs or real-time streaming. The extracted data is then stored in HDFS.
- Data Storage
- After data ingestion, the next step is to store the extracted data. The data either be stored in HDFS or NoSQL database (i.e. HBase). The HDFS storage works well for sequential access whereas HBase for random read/write access.
- Data Processing
- The final step in deploying a big data solution is the data processing. The data is processed through one of the processing frameworks like Spark, MapReduce, Pig, etc.
What is fsck?
fsck stands for File System Check and is a command used by HDFS to check inconsistencies or other issues in a file. For instance, the HDFS gets notified of any missing block by the use of fsdk command.
Which Hadoop tools are used for effective Big Data solutions?
The most important and widely used Hadoop tools that enhance the performance of Big Data are MapReduce writing a paper, Ambari, “Hive”, “HBase, HDFS (Hadoop Distributed File System), Sqoop, Mahout, Pig, Flume, ZooKeeper, NoSQL, Lucene/SolrSee, Avro, Oozie, , GIS Tools, Clouds, and SQL on Hadoop.
What are the various steps in an analytics project?
Various steps in an analytics project include
- Problem definition
- Data exploration
- Data preparation
- Validation of data
- Implementation and tracking
What are some common problems faced by data analyst?
Some of the common problems faced by data analyst are
- Common misspelling
- Duplicate entries
- Missing values
- Illegal values
- Varying value representations
- Identifying overlapping data
What should we deal with suspected or missing data?
To start with, we need to prepare a validation report that lists information like failed validation criteria and date and time of occurrence. A person with enough experience needs to determine the acceptability of this report. Once accepted the invalid data should be assigned and replaced with a validation code. Deletion method, single imputation methods, model-based methods, etc are the best analysis strategies used for the purpose.
Name some statistical methods that are useful for data analytics?
Some of the statistical methods that are useful for data scientist are –
- Bayesian method
- Markov process
- Spatial and cluster processes
- Rank statistics, percentile, outliers detection
- Imputation techniques, etc.
- Simplex algorithm
- Mathematical optimization, etc.
What are the criteria for a good data model?
Criteria for a good data model includes
- Easily consumed
- Should be scalable with further addition of data
- Should have a predictable performance
- Must adapt to changes in requirements