Data Platforms

Question 1

Data-Driven Innovation

Answer 1

Refers to the use of analytics to drive innovation and business value from data

Question 2

Analytics

Answer 2

In this context, we mean the different types of business intelligence initiatives

Question 3

Advanced Analytics

Answer 3

Semi-autonomous examination of data to get deeper insights (Machine Learning)

Question 4

Augmented Analytics

Answer 4

Augment how people explore data with the incorporation of AI

Question 5

Database

Answer 5

Structured and persistent collection of information with efficient retrieval and modification (relational databases)

Question 6

Data Warehouse

Answer 6

Subject oriented collection of data that supports decision making processes

Question 7

OLTP

Answer 7

Constant queries and updates, short term data retention. (Accounting database, online retail transactions)

Question 8

OLAP

Answer 8

Periodic large updates, complex queries for reporting/decision support

Question 9

Data Lake

Answer 9

Central repository system where data is kept in various original formats, unstructured, semi-structured, structured and queried only when needed.

Supports storage, processing and analysis

Question 10

What kind of users use Data Warehouses vs Data Lakes

Answer 10

Business analysts

Vs

Data scientists, data developers, and business analysts

Question 11

What kind of users use Data Warehouses vs Data Lakes

Answer 11

Business analysts

Vs

Data scientists, data developers, and business analysts

Question 12

Data Platform

Answer 12

Meets end-to-end data needs such as acquisition, storage, preparation, delivery, governance and security so users ONLY focus on functional aspects

Question 13

How do we prevent DP from becoming a swamp?

Answer 13

We MUST govern data transformations and leverage metadata and maintenance to keep control over data

Question 14

What are 5 areas of data management? (PCPED) Plankton chokes Patrick every day

Answer 14

1. Data provenance
2. Compression
3. Data profiling
4. Entity resolution
5. Data versioning

Question 15

Data Provenance

Answer 15

Descriptions of origins of data and process by which it arrives

Question 16

Data Provenance Granularity

Answer 16

Fine-grained (instance level)
Coarse-grained (schema level)

Tracking items vs dataset transformations

Question 17

Three levels (types) of data provenance (EAA)

Answer 17

Entity (physical/conceptual thing)
Activity (what generated the thing)
Agent (associated with the activity)

Question 18

Compression

Answer 18

Concise representation of a dataset in a comprehensible manner

Question 19

Data profiling

Answer 19

Analyzing the structure and quality of a dataset ?

Scanned for metadata, completeness and uniqueness of columns, keys and foreign keys

Question 20

Two things data profiling can help with

Answer 20

1. Optimizing queries
2. Cleansing (errors in data)

Question 21

Entity resolution

Answer 21

Find records that refer to the same entity

Question 22

Version Control

Answer 22

Managing changes to computer programs/data collections with a code as the version number.

Question 23

Data versioning

Answer 23

Version control that extends to data models, model parameter tracking and performance comparison

Question 24

Data lakehouse

Answer 24

Flexibility of data lakes and structure of data warehouses (ACID transactions) to combine BI and ML

Vendor lock in…?

Question 25

Data Platform Engineer job description

Answer 25

Implement cloud technologies within data structure of business, in charge of purchasing decisions for cloud services and approval of data architectures

Question 26

DevOpS

Answer 26

Enable software DEVeleopment and operations teams to accelerate delivery with collaboration and iterative improvement

Question 27

DataOps

Answer 27

Use automation to shorten data analytic lifecycle

Question 28

Data fabric

Answer 28

Seamless data access and sharing in distributed environment

Fabric is smooth, unified surface

Question 29

Data mesh

Answer 29

Decentralized, distributed governance and domains owning data products.

Mesh is a grid like surface with interconnected “nodes”/“domains”