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Financial Enterprise Performance > data > Flashcards

data Flashcards

1
Q

Scaling

A

increase the number or decreasing the number

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2
Q

Que decoupling

A

By decoupling data, it helps to remove any implementation dependencies between them. Independent releases. Streamlined and faster development. Improved testability of computing components.

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3
Q

node and instance

A

some sort of machine unit, like a webserver that is processing data in some way

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4
Q

vertical scaling

A

Take one machine and give it more stuff like ram, adding storage, etc.

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5
Q

immutable

A

data will not change

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6
Q

Map Reduce

A

A programming model that is used for bagtch analysis in a wide range of application including: Web analytics, networking, E-commerce, Finance

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7
Q

Map reduce - Map

A

input for the map phase is in key-value pairs. two phase, spliting and mapping out, key, value.

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8
Q

Map reduce - Reduce

A

Output for the reduce phase is in key-value pairs. Reduce shuffle & sort, Reducer - combines key value pairs.

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9
Q

What are four types of Analytics

A
  1. Descriptive Analytics
  2. Diagnostic Analytics
  3. Predictive Analytics
  4. Prescriptive Analytics
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10
Q

Descriptive Analytics

A

What has happened? Example: What is the average number of visitors to a website in a day?

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11
Q

Diagnostic Analytics

A

Why did it happen? Example: What is the reason that this patients heart failed at exactly 12:03 PM?

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12
Q

Predictive Analytics

A

What is likely to happen? Example: When will the stock prices for Amazon begin to go down again?

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13
Q

Prescriptive Analytics

A

What can we do to make it happen? Example: What is the best route to drive to Alderwood Mall at 5:00 PM?

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14
Q

what is Big Data?

A

Collections of datasets whose volume, velocity and variety is so large that it is difficult to store, manage, process and analyze the data using traditional databases and data processing tools.

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15
Q

How BIG is Big Data?

A

2.5 quintrillion bytes of data every day

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16
Q

5 Characteristics of Big Data

A

Volume
Velocity
Variety
Veracity
Value

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17
Q

Volume

A

how much

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18
Q

Velocity

A

How fast

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19
Q

Variety

A

structured, unstructured and semi-structured

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20
Q

Veracity

A

refers to the biasedness, noise, and abnormality in data. It also refers to incomplete data or errors, outliers, and missing values.

21
Q

Value

A

Usefulness

22
Q

Analytics Flow for Big Data

A

Data Collection
Data Preparation
Analysis Types
Analysis Modes
Visualizations

23
Q

Data Collection

A

Connectors:
 Publish-Subscribe messaging frameworks
 Messaging queues
 Source-sink connectors
 Database connectors

24
Q

Data Preparation

A

Data Cleaning
 Wrangling/Munging
 De-duplication
 Normalization
 Sampling
 Filtering

25
Q

Analysis Types

A
  • Basic statistics
  • Regression
  • Recommendation
  • Classification
  • Clustering
  • Text Analysis
  • Pattern Mining
26
Q

Analysis Modes

A

 Batch (useful if you do not need results right away)
 Real-time (useful if you need results right away)
 Interactive (useful if you need results right away and
want the user to be able to change the parameters of the
analysis)

27
Q

Visualizations

A

 Static (use if you just want to display the results)
 Dynamic (results need to be updated regularly)
 Interactive (results needs to be updated regularly and
receive input from the user)

28
Q

Big Data Stack

A

The Big Data Stack is a series of applications that are used to accomplish the analytics flow just mentioned.
 These applications can be stored on one server/computer or are multiple ones and accessed across a network.
 Hadoop can usually handle most of the tasks but not always. We will use the computational tasks association to
figure out which it can and which it can’t.

29
Q

Hadoop

A

Hadoop is an open-source framework for distributed batch processing of massive scale data using the MapReduce
programming model.

30
Q

MapReduce

A

programming model useful for big data that won’t fit on a single machine. MapReduce’s magic comes from the fact that it does computation at the location of the files instead of transferring the data.

31
Q

Big Data
Stack
Overview

A

Raw Data Sources
Data Access Connectors
Data Storage
Batch Analytics
Real-time Analytics
Interactive Querying
Serving Databases, Web & Visualization Frameworks

32
Q

Data Access Connectors

A

publish-subscribe messaging, source-sink connectors, datbase connectors, Messaging Quess, Custom Connectors

33
Q

Data Storage

A

Distributed Filesystem (HDFS)
 Optimized for MapReduce to be used with it.
NoSQL (Hbase, MongoDB)
 Stands for “Not-Only-SQL”.
 SQL-type code that has programmatic
capabilities.

34
Q

Analytics Patterns

A

Alpha Pattern
Beta Pattern
Gamma Pattern
Delta Pattern

35
Q

Alpha Pattern

A

Batch Analysis, Data Storage - relational or non-relational databases. Examples - Web Analytics, weather monitoring

36
Q

Beta Pattern

A

Real-time analysis. Examples - internet of Things applications and real-time monitoring applications

37
Q

Gamma Pattern

A

Combines Batch and real time

38
Q

Delta Pattern

A

Interactive Querying. Examples - web analytics, advertisement targeting, inventory management and enterprise applications.

39
Q

Analytics Architectures

A

Load Leveling with Queues
Load Balancing with Multiple Consumers
Lambda Architecture
Scheduler-Agent-Supervisor

40
Q

Queue

A

A queue is a data structure that holds data that is executed upon one element at a time.

41
Q

Advantages

A

 Better Horizontal scaling capability
 Better performance for big data
 Works well with unstructured data
 Optimized for real-time performance
 Designed for fast retrieval

42
Q

Disadvantages

A

 Still in development (newer than relational DBs)
 We will see some disadvantages too as we explore their
functionality

43
Q

No SQL - Key-Value Databases

A

-Stores data in the form of key-value pairs
 Keys are used to uniquely identify the values stored
 Keys are also used to determine where the value should be stored
-Distributed architectures comprising of multiple storage
nodes
 Data partitioned across storage nodes with the keys
 Hash functions are used to determine the partition number for the
key

44
Q

No SQL - Document Databases

A

 Used to store semi-structured data in the form of
documents
 Documents are encoded in a variety of standards including
JSON, XML, BSON, or YAML
 These are all just forms of semi-structured data languages. We’ll
see some examples
 Semi-structured data: documents stored are similar to each
other but there is no strict schema.

45
Q

Benefit of using document DBs over key-value DBs

A

he
querying is more efficient based on the attribute values in
the documents

46
Q

No SQL - HBase

A

 Scalable linearly with the addition of new nodes
 Distributed
 Column family usage
 Provides structured data storage for large tables
 Can store both structured and unstructured data

47
Q

Compaction Types

A

 Minor: merges the small files into a single files
when number exceeds a threshold.
 Major: merges all store files into a single large
store file.
The outdated and deleted values (Tombstone
marked) are removed.

48
Q

Bloom Filters in HBase

A

 Bloom Filters determine if an element is in a
particular set.
 Bloom Filters work with HBase to exclude store
files that need to be looked up while serving read
requests for a particular row key.
 Basically, Bloom Filters make the lookup process
more effective by reducing the amount to be
searched through.

49
Q

Graph Databases

A

Graph structure with nodes and links. Think Social media.

Financial Enterprise Performance (1 decks)

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