is the field of developing, testing, deploying and maintaining data processing solutions via collecting, parsing, transforming, joining processing and managing data

make data avalaible for data scientists to develop models and data products

two main activities that comprise data _____________include storage and processing of data

is tasked with making data amenable to various types of data analyses, including model development (data mining and other business-process specific algorithms) and reporting)

two main activities that comprise ____________ include storage and processing of data, wich is typically structured.

within the realm of__________________ involves developing highly distributed, scalable, fault-tolerant data processing solutions to process large amounts of data in order to garner insights

comprises data processing in support of the Big Data analysis lifecycle.

make data available for data scientists to develop models and data products

They are required to have knowledge of various data storage and processing technology alternatives for acquiring, storing and processing tecnology alternatives for acquiring, storing and processing data that is often semi-structured and unstructured in nature

comprises data processing in support of the big Data analysis lifecycle

importing/exporting large amounts of data from to traditional storage technologies, including OLTP (CRM, ERP, SCM systems) and OLAP systems (data warehouse)

field of developing, testing deploying and maintaining data processing solutions via collecting, parsing transforming, joining, processing and managing data

validating and cleansing data in realtime or batch mode and creating efficient data models

stablishing an optimal data storage and processing enviroment based on the type of data and its processing requirements

developing efficient data processing algorithms that run over cluster of computers

developing Big Data pipelines and Big Data applications that may include meaningful data visualizations

storage devices nd

analytics engine

processing engine

resource manager

disk_based

realtime

batch

memory_based

When data gets processed as a result of an ETL activity or uotput in generated as a result of an analytical operation

When datasets are acquired or when data gets generated inside the enterprise boundary

In realtime processing mode, data is first processed in memory and then stored to the disk

WHen data is manipulated for making it amenable to data analysis

is stored on a separate node and each node is responsible for only the data stored on it

all_______ collectively represent the complete database

shares the same schema

stores muktiplecopies of a database

____________ distributes a processing load across multiple nodes to achieve horizontal scalability

___________ may or not be transparent to the client

supports horizontal scaling as a method for increasing resource capacity. This is accomplished by adding similar or higher capacity resources alongside the existing resources

Once saved, the data is replicated over to multiple slave nodes

SInce each node is responsible for a part of the whole dataset, read/write times are greatly improved

shard

master-slave

peer-to-peer

partition

Replication

peer-to-peer

master-slave

ideal for read intensive loads rather than write intensive loads, as growing read demands can be fulfilled simply via horizontal scaling wich adds additional slave nodes

Writes are consistent as all writes are coordinated by the master node. This means that write performance suffers as the amount of writes increases

Since each node is responsible for a part of a whole dataset, read/write times are greatly improved

in case the master node fails, reads are still possible via any of the slave nodes

a slave node can be configures as d backup node for the master node

In case of master node failure, writes are not supported until a master node is reestablished. It is either resurrected from a backup of the master node, o a new master node is chosen from slave nodes.

For example, queries requiring data from multiple shards will impose performance penalties. Data locality, or keeping commonly accesed data collocated on a sngle shard, helps to counter such performance issues

Read inconsistency can be an issue it a slave node is read prior to the update being propagated over to it from the master node

To ensure read consistency, a voting system can be implemented where a read is declared consistent if the majority of the slave nodes contain the same version of the record. Implementation of such a voting system required reliable and fast communication mechanism between the slave nodes

read inconsistency can be an issue if a principal node is read prior the update being propagated over to it

Each write is copied to all peers

prone to write inconsistencies that occur as a result of a simultaneous update of the same data across multiple ___

This can be addessed by implementing pessimistic or optimistic concurrency.

Reads can be inconsistent between the time period when some of the ____ have been update while the others are being updated. However, reads eventually become consistent when the updates have been copied over to all ____

Ideal for read intensive loads rather than write intensive loads, as growing read demands can be fufilled simply via horizontal scaling wich adds additional ____ nodes

To ensure read consistency, a voting system can be implemented where a read is declared consistent if the majority of the _ contain the same version of the record

Implementation of such a voting system requires a reliable and fast communication mechanism between the ____

Althoug more than one master is possible, a single slave-shard can only be managed by a single master-shard

Replicas of shard are kept on multiple slave nodes to provide scalability and fault tolerance for read operations

Write consistency is mainteined by the master-shard

however, this means that fault tolerance Ç(with regards to write operations) is affected if the master-shard becomes non-operational or a network outage occurs

Each shar gets replicated to multiple peers, and each peer is only responsible for subset of data rather than the complete dataset

Colelctivelly, this helps to achieve increased sacalability and fault tolerance

However, this means that fault tolerance (with regards to write operations) is affected if the master-shard becomes non-operational or a network outage occurs

As there is no master involved, there is no single point of failure with regards to both read and write operations

Consistency

parttition tolerance

Availability

volumen

velocity

If consistency (C) and availability (A) are required, available nodes need to communicate to ensure consistency (C). Therefore, partitio tolerance (P) is not possible.

If consistency (C) and partition tolerance (P) are required, nodes cannot remain available (A) as the nodes will become unavailable while achieving consistency (C) wich cannot happen while supporting partition tolerance (P)

If availability (A) and partition tolerance (P) are required then consistency (C) is not possible because of the data communication requirement between the nodes. So, the database can remain available (A) but with inconsistent results

In a distribute database, scalability and fault tolerance can be improved through additional nodes, although this challenges consistency (C), which can also cause availability (A) to suffer due to the latency caused by increased communication between nodes.

Distributed database systems cannot 100% partition tolerant (P). Although communication outages are rare and temporary, partition tolerance (P) must always be sopported, Thus, CAP is more about being either CP or AP

On the other hand, RDBMSs are CA as they are generally available (A) while being consistent (C) at the same time. RDBMSs generally run on a single node, thus partition tolerance (P) is not a large consideration

Availability

Atomicity

Consistency

Isolation

Durability

Basically available

Consistency

Soft state

Eventual consistency

scalability

soft consistency

Redundancy & availability

Fast access

Long-term storage

Schema-less storage

Inexpensive Storage

big data datasets come in huge volumes at a fast pace and are generally acquired from multiple sources. This results in large amounts of data within a short period of time

With the potential of findidng new insights about the way businesses work, enterprises are retaining more and more data , both generated inside the enterprise and obtained from outside sources, as part of their data acquisition activities.

Traditionally, historic data that is no longer required is generally archived in offline storage. However, this makes the historic data unavailable for instant analysis

It may not be possible to re-acquire data due to expensive acquisition costs, for example when a dataset is purchased from a data provider.

Traditionally, historic data thata is no longer required is generally archived in offline storage. However, this makes the historic data unavailable for instant analysis

it may not be possible to re-generate data if the events that generated the data initially were one-off events, for example a smart meter reading for a certain point in time

In all cases, we need a scalable storage solutions with enough capacity for current and future data capture requirements

Apart from storing the raw data, additional storage is required to store the data created as a result of the data wrangling activity

A storage device needs to make efficient use of the underlying disk resources to minimize storage waste.

The data storage requirement, inthe case of joining multiple datasets, will increase in size due to the need to keeo both the original datasets and the joined dataset

Storage is further required in order to persist the analytic results from a data analysis activity

in order to address the increasing demands for data storage, a storage device can either scale up or scale out

is a strategy for increasing resource capacity by replacing an existing low capacity device with a higher capacitty device.

does not cause disruption and system downtime

For example to double the capacity of a storage device, data from a 500 GB disk can be copied over to 1 TB disk.

will cause disruption and system downtime

is a strategy for increasing resource capacity by replacing an existing low capacity device with a higher capacitty device.

is a strategy for increasing resource capacity by adding similar or higher capacity commodity devices alongside the existing device

For example, to double the storage capacity, a 500GB disk can be added alongside an existing 500GB disk

Does not cause disruption and system downtime

Big Data datasets, both in raw and processed form, are a business asset and require attention with regards to storage. Multiple business functions may need to glean value out of these datasets, cometimes simultaneously

Big data datasets come in huge volumes at a fast pace and are generally acquired from multiple sources. This results in large amounts of data within a short period of time.

This dependence on Big Data datasets across an enterprise requires a reliable storage device that is fault-tolerant and is highly available

A big data solution enviroment is generally composed of cluster that are built using commodity servers connected via a high bandwidth network. With more nodes and network connections, the chances of a node becoming unavailable increases either due to hardware breakdown such as disk failure, or due to network failure.

As a result, storage redundancy is required to ensure auninterrupted acces to data in the event of a storage device failure, thereby providing high availability and fault tolerance

Sharding can help create scalable storage as shards can be stored on the nodes added via scaling out

to provide such redundancy, a storage device implements automatic sharding and replication with a configuration of either sharding and master-slave replication or sharding and peer-to-peer replication

realtime processing

batch processing

in_memory

in disk

Big Data analytics generally involve both realtime as well as batch processing

Realtime data analysis requires fast read/write capabilities that are usually implemented via in-memory storage solutions.

Batch processing requires stream-based data access with high throughput, implemented via traditional disk-based storage devices or newer solid state drives that provide better performance at the expense of higher cost

The basic tenet of Big Data is to extract value from large amounts of data

At the same time, data arriving at high velocity needs a storage device that supports fast writes with minimal overhead. for example, schema validation at read time instead of write time

The basic tenet of Big Data is to extract value from large amounts of data

Realtime data analysis requires fast read/write capabilities that are usually implemented via in-memory storage solutions

This requires enterprises to retain data for longer periods of time (to create larger datasets) so that future data analyses are more insightful due to having more historic data available

This characteristic warrants the need for a storage device with increased storage capacity that is reliable and can be brougth online without incurring too much time delay

Traditionally, historic data that is no longer required is generally archived in offline storage. however, this makes the historic data unavailable for instant analysis.

big data analytics require historic data to be available online for discovering hidden patterns leading to valuable insights

data arriving at high velocity needs storage device that supports fast writes with minimal overhead, for example, schema validation at read time instead of write time

As a result, historic data is kept online by adding more storage capacity via scaling out

As a result, historic data is kept online by adding more storage capacity via scaling out

big data datasets come in multiple formats with limited or no schema, such as semi-structured and unstructured data.

without any prior knowledge about the structure of data does not guarantee that the data would conform to the same structure in the future, as in the nature of unstructured data

This requires the storage device to support __________ data persistence along with the added ability to make schema changes on the fly without breaking existing applications or incurring downtime

With all the characteristics required to store voluminous data, provide replication, and support long-term storage, the cost of storage devices can become a concern.

without any prior knowledge about the structure of the data does not guarantee that the data would conform to the same structure in the future, as is the nature of unstructured data

A storage device needs to make efficient use of the underlying disk resources to minimize storage waste.

Use of proprietary storage devices generally requires replacement of existing nodes with more expensive ones in order to scale up, eventually hitting a limit

A storage device needs to make use of commodity hardaware that can scale out so that costs can be kept down as enterprises amass more and more data

distributed file system

database

file system

NoSQL

A storage device that is implemented with a __________ provides simple, fast access data storage that is capable of storing large datasets that are non-relational in nature, such as semi-structured and unstructured data

To handle large volumes of data arriving at a fast pace, relational databases generally need to scale.

Although based on straightforward file locking mechanisms for concurrency control, it provides fast read/write capability, which addresses the velocity characteristic of Big Data.

are good fit when data must be accessed in streaming mode with no random reads and writes.

is not ideal for datasets comprising a large number of small files as this creates excessive disk-seek activity, slowing down the overall data access

Is also more overhead involved in processing multiple smaller files, as dedicated processes are generally spawned by the processing engine at runtime for processing each file before the results are synchronized from across the cluster

good for handling transactional workloads involving small amounts of data with random read/write

work best with fewer but larger files accessed in a sequential manner

Multiple smaller files are generally combined into a single file to enable optimum storage and processing

Do not provide any file searching capability out of box

Can be employed in clustered enviroment.

____ storage device is suitable when large datasets of raw data to be stored or when archiving of datasets is required

provides an inexpensive storage option for storing large amounts of data over a long period of time that remains online

Good for handling transactional workloads involving small amounts of data with random read/write

More disk can simply be added without needing to offload the data to offline data storage, such as tape drives

like any file systems, are agnostic to the data being stored and therefore support schema-less data storage

provides out of box redundancy and high availability by copying data to multiple locations via replication

Relational databases or relational database management systems (RDBMSs)

File systems

non-relational databases or Not only SQL (NoSQL)

NewSQL

Are good for handling transactional workloads involving small amounts of data with random read/write

Schema-less data model-Data can exist in its raw form

Are ACID, and in order to honor this compliance, they are generally restricted to a single node

The redundancy and fault tolerance provided by sharding and replication in a clustered enviroment are not inherently supported.

To handle large volumes of data arriving at a fast pace relational databases generally need to scale

Employ vertival scaling, not horizontal scaling, wich is a more costly and disruptive scaling strategy. This makes _______ less than ideal for long term storage of data

More disks can simply be added without needing to offload the data offline data storage, such as tape drives

Note that some relational databases, for example IBM DB2 pureScale, Sybase ASE Cluster Edition, Oracle Real Application Clusters (RAC) and Microsoft Parallel Data warehouse PDW, are capable of being run on clusters. however, these database clusters still use shared storage that can act as a single point of failure

Need to be manually sharded, mostly using application logic.

this means that the client (applicaction logic) needs to know which shard to query in order to get the required data

Reads across multiple nodes may not be consistent inmediately after a write. however, all nodes will eventually be in a consistent state

This further complicates the data processing when data from multiple shards is required

generally require data to adhere to a schema. As a result, storage of semi/unstructured data whose schema is not know or keeps changing is not supported

Refers to tecnologies used to develop net generation non_relational databases that are highlly scalable and fault-tolerant

The schema conformance is validated at the time of data insert/update,wich introduces overhead and leads to latency

A less ideal choice for storing high velocity data that needs a highly available database storage device with fast data write capability

Is not useful as a storage device in a Big data solution enviroment

Schema-less data model: Data can exist in its raw form

scale out rather than scale up: More nodes required rather than replacing the existing one with better higher performance node

Highly available: Built on cluster-based technologies providing fault tolerance out of box

Lower operational costs: Built on open source platforms with no licensing costs, and can be deployed on commodity hardware.

Eventual consistency; Reads across multiple nodes may not be consistent immediately after a write. However, all nodes will eventually be in a consistent state

BASE, not ACID: BASE compliance requires a database to maintain high availability in the event of network/node failure, while not requiring the database to be in a consistent state whenever an update occurs. The database can be in a soft/inconsistent state until it eventually attains consistency.

API driven data access - Data access is generally supported via API based queries, including RESTful APIs. Whereas some
implementations may also provide SQL-like query capability

Auto Sharding and replication -To support horizontal scaling and provide high availability, a NoSQL storage device automatically employs sharding and replication techniques where the dataset is partitioned horizontally and then copied over to multiple nodes

Integrated caching - Removes the need for a third-party distributed caching layer, such as Memcached

Distributed query support - NoSQL storage devices maintain consistent query behavior across multiple shards

Polyglot persistence - The use of NoSQL device storage does not mandate retirin traditional RDBMSs. In fact, both can be used at the same time, thereby supporting polyglot persistence (an approach of persisting data using different types of storage technologies). this is good for developing systems requiring structured as well as semi/unstructured data.

Aggregate-focussed - Unlike relational databases that are most effective with fully normalized data, NoSQL storage devices store de-normalized data aggregated data (an entity containing merged, often nested, data for an object) Thereby eliminating the need for joins and mapping between application objects and the data stored in the database. Note that graph database storage devices are not aggregate-focused

The storage requirement of ever increasing data volumes commands the use of databases that are highly scalable while keeping costs down for the business to remain competitive.

The fast influx of data requires databases with fast access data write capability

NoSQL storage devices fulfill this requirement by providing scaling out capability while using inexpensive commodity servers

Furthermore, there may not be licensing costs involved as NoSQL databases generally follow the Open Source development model

NoSQL storage devices fulfill this requirement by providing scaling out capability while using inexpensive commodity servers

The fast influx of data requires databases with fast access data write capability

NoSQL storage devices enable fast writes by using schema-on-read rather than schema-on-write principle

Being highly available, NoSQL storage devices can ensure that write latency does not occur because of node/network failure

A storage device needs to handle different types of data formats including documents, emails, images and videos and incomplete data

NoSQL storage devices enable fast writes by using schema-on-read rather than schema-on-write principle

NoSQL storage devices can store these different forms of semi-structured and unstructured data formats

At the same time, NoSQL storage devices are able to store schema-less data and incomplete data with the added ability of making schema changes as the data model of the datasets evolve. In other words, NoSQL databases support schema evolution

Key-value

RDBMS

column-family

Document

Graph

Act like hash tables

Is a list of values each value is identified by a key

indexes that speed up searches are generally supported

The value is opaque to the database and is essentially stored as a BLOB

The value stored can be aggreted, ranging from sensor data to videos

Value look-up can only be performed via the keys as the database is oblivious to the details of the stored aggregate

Partial updates are not possible. An update is either a delete or an insert operation

A select operation can retrieve a part of the aggregate value

_______ storage devices generally do not maintain any indexes, therefore writes are quite fast

______ Based on a simple storage model, storage devices are highly scalable

The key is usually appended with the type of the value being saved for easy retrieval. for example 123_sensor1

some implementations support compressing values for reducing the storage footprint. However, this introduces latency at read time, as the data needs to be decompressed firts

most key-values storage devices provide collections or bucket(like tables) into wich key-value pairs can be organized

can be encoded using either a text-based encoding scheme, such as XML, or JSON or BSON (binary)

Unstructure data storage is required

high performance read/writes are required

the value is fully identifiable via the key alone

searches need to be performed on different fields of the document

value is a standalone entity that is not dependent on other values

values generally have a comparatively simple structure or are binary

query patterns are simple. involving insert, select and delete operations only

stored values are manipulated at the application layer

applications require searching or filtering data using attributes of the store value

the value is fully identifiable via the key alone

relationship exist between different key-value entries

a group of keys values need to be updated in a single transaction

multiple keys require manipulation in a single operation

schema consistency across different values is required

update to individual attributes of the value is required

examples: Riak,Redis and amazon Dynamo DB

The table is a list of values where each value is identified by a key

Also store data as key-value pairs. However, unlike key-value storage devices, the stored value is a document that can have a complex nested structure, such as an invoice

Can be encoded using either a text-based encoding scheme, such as XML or JSON, or using a binary encoding scheme, such as BSON (binary JSON)

Like key-value storage devices, most document storage devices provide collections or buckets (like tables) into which key-value pairs can be organized

document storage devices are value-aware

The stored value is self-describing: the schema can be inferred from the structured of the value

A select operation can reference a field inside the aggregate value

A select operation can retrieve a part of the aggregate value

partial update are supported; a subset of the aggregate can be updated

indexes that speed up searches are generally supported

Each document canhave a different schema

The value is opaque to the database and is essentially stored as a BLOB

Is possible to store different types of documents or documents of the same type that have fewer or more fields with respect to each other

Additional fields can be added to a document after the initial insert, thereby manifesting flexible schema support

It should be noted that document storage devices are not limited to storing data that occurs in the form of actual documents, such as an XML file, but can also be used to store any aggregate that consists of a collection of fields having a flat or a nested schema

Storing semi-structured document-oriented data comprising flat or nested schema

schema evolution is a requirement as the structure of the document is either unknown or is likely to change

applications require a partial update of the aggregate stored as a document

searches need to be performed on different fields of document

unstructured data store is required

storing domain objects, such as customers, in object form

query patterns involve insert, select, update, and delete operations

Multiple documents need to be updated as part of a single transaction

storing domain objects, such as customers, in object form

Performing operations that need joins between multiple documents or storing data that is normalized

schema enforcement for achieving consistent query design is required as the document structure may change between successive query runs, wich will require retructuring the query

stored value is not self-describing

binary data needs to be stored

Column-family storage devices store data much like a traditional RDBMS but group related columns together in a row, resulting in column-families

The table is a list of values where each value is identified by a key

Each column can be a collection of related columns itself, referred to as a super-column

Each super-column can contain an arbitrary number of related columns that are generally retrieved or updates as a single unit

Each row consists of multiple column-families

Each row can have a different set of columns, thereby manifesting flexible schema support

Each row is identified by a row key

provide fast data acces with random read/write capability

Store different column in separate physical file, wich greatly helps in speeding up queries as only the required column-families are searched

Partial update are not possible. An update is either a delete or insert operation

provide support for selectively compressing

Leaving searchable ______ uncompressed can make queries faster because the target column does not need to be decompressed for lookup

Most implementations support data versioning after wich the configured columns are automatically removed

Realtime random read/write capability is needed and data being stored has some defined structure

Data represents a tabular structure, each row consists of a large number of columns and nested groups of interrelated data exist

Support for schema evolution is required as column families can be added or removed without any system downtime

storing domain objects, such as customers, in object form

Certain fields are mostly accessed togheter, and searches need to be performed using field values

efficient use of storage is required when the data consists of sparsely populated rows (no column, no space)

query patterns involve insert, select, update, and delete operations

Multiple documents need to be updated as apart of a single transaction

relational data access is required, for example joins

ACID transactional support is required

binary data needs to be stored

SQL-compliant queries need to be ecxecuted

query patterns are likely to change frequently that initiate a corresponding restructuring of how column-families are arranged, for example, during proof of concept development

MongoDB, CouchDB and terrastore

NoSQL:Column-Family

Riak, Redis and Amazon Dynamo DB

Persist inter-connected entities

The value is opaque to the database and is essentially stored as a BLOB

Unlike other NoSQL storage devices, where the emphasis is on the structure of the entities, ____________storage devices place emphasis on storing the linkages between entities

Entities are stored as nodes, also called vertices, while the linkages between entities are stored as edges. In RDBMS parlance, each node can be thought of a single row while the edge denotes a join

Nodes can have more than one type of link between them through multiple edges.

Each node can have attribute data as key-value pairs, such as a customer node with ID, name and age attributes

Each edge can have its own attribute data as key-value pairs, wich can be used to further filter query results

Multiple edges asre similar to defining interconnected nodes based on node attributes and/or edge attributes, commonly referred to as node traversal

Queries generally involve finding interconnected nodes based on node attributes and/or edge attributes, commonly referred to as node traversal

Can be a collection of related columns itself referred to as a super-column

Edges can be unidirectional or bidirectional, setting the node traversal direction

Generally, graph storage devices provide consistency via ACID compliance

The value stored can be any aggregate, ranging from sensor data to videos

The degree of usefulness of a graph storage device depends on the number and types of edges dened betwwen the nodes. The higher the number and more diverse the edges are the more diverse types of queries it can handle

As a result, it is important to comprehensively capture the types of relations tha exist betqeen the nodes

Generally allow adding new types of nodes without making changes to the database, it also enables defining additional links between nodes as new types of relationships or nodes apperar.

interconecccected entities need to be stored

querying entities based on the type of relationship with each other rather than the attributes of the entities

finding groups of interconnected entities

finding distances between entities in terms of the node traversal distance

mining data with a view toward finding patterns

storing semi-structured document-oriented data comprising flat or nested schema

updates are required to a large number of node attributes or edge attributes, as this involves searching for nodes or edeges wich is a costly operation compared to performing node traversals

binary storage is required or else queries based on selection of node/edge attributes dominate node traversal queries

Entities have a large number of attributes or nested data- is the best store ligtweight entities in a graph storage dev ice while storing the rest of the attribute data in a separate non-graph NoSQL storage device

ACID transactional support is required

NoSQL storage devices are highly scalable, available fault-tolerant, and very fast for read/writeoperations. However, they do not provide the same transaction and consistency support as exhibited by ACID compliant RDBMS

Following the BASE model, NoSQL storage devices provide eventual consistency rather than immediate, and could therefore be in an inconsistent stage while reaching the state of consistency. As a result, they cannot be used for implementimg large scale transactional systems

____ storage devices combine the ACID properties of RDBMS with the scalability and fault tolerance offered by NoSQL storage devices

Each super-column can be a collection of related columns itself referred to as a super-column

______ databases generally support SQL compliant syntax for data definition and data manipulation operations, and they often use a relational data model for data storage

_____________ can be used for developing OLTP systems with veri high volumes of transactions, for example a bank. They can also be used for realtime analytics, for example operational analytics, as some implementations are memory based

As compared to a NoSQL storage device, _________ storage device provides an easier transition from a traditional RDBMS to a highle scalable database due to its support for SQL

big Data datasets are generally stored utilizing distributed technologies (distributed file system or NoSQL database)

The very nature of a distributed storage device requires a processing engine that can process data without needing to transfer the data from storage to a computing resource as with distributed data processing

Schemas may change over time in a effort to accomodate changing bussines requirements, or simply because of an application software upgrade

In support of maximizing the value characteristics of Big Data, it is imperative to employ a processing model based on the divide-and-conquer principle, as with parallel data processing

often, it may be feasible to process data offline in batches, such as with overnight report generation

big Data datasets come in multiple formats (variety characteristic) and may not conform to any schema, especially unstructured data

____________may change over time in an effort to accomodate changing business requirements, or simply because of an application software upgrade.

The lack of adherence to any particular data model requires flexible processing of Big Data datasets so that they can be processed in raw form without the need to be stored in a particular data model

Big data datasets may arrive thick (volume characteristic) and/or fast (velocity characteristic)

Often, it may feasible to process data offline in batches, such as with overnight report generation

In other cases, the results may be required in in realtime, such as with GPS signal processing workloads (transactional and batch)

In order to achieve maximum value from Big Data datasets the underlying processing platform may need to support both transactional and batch workloads

A single processing engine may fulfill this requirement, or multiple processing engines may need to be used

Similarly, more data sources with unknown schemas may need to be added in the future

Although having the ability to process data both in realtime (transactional) and batch mode is ideal for extracting maximum value out of Big Data datasets, support for both modes may not be required or may not be feasible

the processing of Big data datasets requires a highly scalable processing engine that can be linearly scaled

Generally, assembling a batch processing Big Data solution enviroment is simpler and cheaper when compared to a realtime processing Big Data solution

Consequently, adding support for multi-workload processing should be business driven involving careful cost-benefit analysis

Owing to the volume and velocity characteristics of Big Data, the processin demand can grow quite sharply with increasing volumes of data arriving at a fast pace

supporting a distributed processing enviroment with parallel processing capabilities requires a processing engine that can provide a steady throughput as the data volumes grow

The processing of Big Data datasets requires a highly scalable processing engine that can be linearly scaled

Big Data datasets may arrive thick (volume characteristics) and/or fast (velocity characteristics)

In the context of processing, linear scalability means that one receives a proportional increase in performance with the addition of more processing nodes

Realtime business intelligence and analytics can leverage such a linearly scalable processing enviroment to deliver quicker responses involving complex operations on an entire dataset

_____________ is generally achieved by employing a scaling out strategy as it provides a simple, non-disruptive, and cost effective method for increasing processing capacity

A highly distributed data processing enviroment with parallel data processing capabilities generally involves complicated architecture

With a horizontally scaled processing architecture, wich by desing involves a large number of nodes and networking components, the chances of partial system failure increase

Similarly, more data sources with unknown schemas may need to be added in the future

As a system failure in the middle of a long running distributed task would be detrimental to achieving the analytic goals, aprocessing engine needs to provide fault tolerance so that a partial failure does not render the entire system unavailable and data processing does nort need to started from scratch

Generally, fault tolerance is provided through redundancy

With redundant processing resources, the system can still be available in the event of a partial system failure

Horizontal scaling lends itself quite useful in this case as redundancy can generally be increased by simply adding more processing resources

At the start of a Big Data initiative, the cost for a highly scalable distributed data processing enviroment involving a few processing nodes and networking equipment may not be that high.

Over time, as the data volume grows and the types and frequency of analytics being run increase, the requirement for an increased number of processing resources can translate into soaring IT costs involving both software and hardware

With redundant processing resources, the system can still be available in the event of partial system failure.

This could prove counterproductive to the very reason the Big Data initiative was undertaken (often to help the business deliver increased value, drive down costs, find new sources of reveneu, or establish new service offerings)

Use a open source software deployed over commodity hardware helps to keep the costs down

Another aspects of keeping costs down is the ability of the processing engine to take advantage of cloud

The on-demand and elastic nature of the cloud helps avoid any up-front capital investment, coupled with faster setup of the data processing solution enviroment

____________ large amounts of data is not a new phenomenon, and different large scale data processing architectures exist

______________________ requires a distributed enviroment that is capable of processing data in parallel a characteristic supported by the cluster architecture

are highly scalable, supportin g horizontal sclaing with linear performance gains

is a group of nodes connected together via a network to process tasks in parallel

__________ is a centrally managed network of nodes (computers) where each node is responsible for a sub-task of a large problem

enable distributed data processing

_____comprises low-cost commodity nodes that collectively provide increased processing capacity with inherent redundancy and fault tolerance, as it consists of physically separate nodes

The majority of Big Data processing occurs

are higly scalable, supporting horizontal scaling with linear performance gains

Provide an ideal deployment enviroment for a processing engine as large datasets can be divided into smaller datasets and then processed in parallel in a distributed manner

Can be utilized both by a realtime processing engine and a batch proceessing engine, such as Spark and MapReduce respectively

data is processed offline in batches where the response time could vary from minutes to hours

Data is first persisted to the disk and only then processed

Strategic BI predictive/prescriptive analytics and ETL operations are generally _____________________

data is processed in-memory as it is captured before being persisted to the disk

______________involves processing a range of large datasets, either on their own or joined together, essentially addressing the volume and variety characteristics of big Data datasets

the majority of big data processing occurs in ______________________________

______________ is relatively simple, easy to set up, and low cost in comparison to realtime mode

_____________________ data is processed in -memory as it is captured before being persisted to the disk

Response time generally ranges from a few seconds to under a minute

strategic BI, predeictive/prescriptive analytics and ETL operations are generally batch-oriented

______________ adresses the velocity characteristic of Big Data dataset

_____________________ is alsocalled event or stream processing as the data either arrives continuously (stream) or at intervals (event)

The individual event/stream datum is generally small in size, but its continuous nature results in very large datasets

Another related term, interactive mode, falls within the category of realtime, interactive mode generally

Operational BI/analytics are generally

is a widely used implementation of the batch processing engine mechanism

It is a highly scalable and reliable processing engine based on the principle of divide-and-conquer

It provides built-in fault tolerance and redundancy

it divides a bigger problem into a set of smaller problems that are easier and quicker to solve

it has its roots both in distributed computing as well as in parallel computing

Operational BI/analytics are generally conducted in realtime mode

_____________ is a batch -oriented processing engine used to process large datasets using parallel processing deployed over clusters of commodity hardware

with redundant processing resources, the system can still be available in the event of a partial system failure

_____________ does not require the input data to conform to any particular data model. Therefore, it can be used to process schema-less datasets

A dataset is bloken down into multipl smaller parts and operations are performec on each part independently and in parallel

The results from all operations arre then summarized to arrive at the answer

____________ processing engine generally supports batch workloads only

_______, the data processing algorithm is instead moved to the nodes that store the data

The data processing algorithm executes in parallel on these nodes, thereby eliminating the need to first move the data

This not only saves network bandwidth but also results in a large reduction in processing time for large datasets, as processing smaller chunks of data in parallel is much faster

Reads across multiple nodes may not be consistent immediately after a write. However, all nodes will eventually be in a consistent state

low cost

map : map task

combine (optional) : map task

partition : map task

shuffle & sort : Reduce task

reduce : Reduce task

linear scalability

The firts stage of map reduce, during wich the dataset file is divided into multiple smaller splts

Each split is parsed into its constituent records as a key-value pair

The processing of Big Data datasets requires a highly scalable processing engine that can be linearly scaled

The key is usuallu the ordinal position of the record and the value is the actual record. For exampl (234, sky is blue)

The parsed key-value pairs for each split are then sent to a map function (mapper), with one mapper function per split. The map function executes user-defined logic

Each split generally contains multiple key-value pairs and the mapper is run once for each key-value pair in the split

The mapper processes each key-value pairs as per the user-defined logic and further generates as a key-value pair as its output

The output key can either be the same as theinput key or a substring value from the input value, or another serializable user-defined object

similarly, the output value can either be the same as the input value or a substring value from the input value, or another serializable user-defined object

generally, the output of the map functions is handled directly by the reduce function.However, map task and reduce tasks are mostlyrun over different nodes

Generally, the output of map function is handled directly by the reduce function. However, map tasks ans reduce tasks are mostly run over different nodes

Requires moving data between mappers and reducers that can consume a lot of valuable bandwidth, and directly contributes to processing latency

With larger datasets, the time token to move the data between map and reduce stages can exceed the actual processing undertaken by the map and reduce tasks

For this reason the mapReduce engine provides an optional______________________ function that sumarizes a mapper´s output before it gets processed by the reducer

The first stage of MapReduce is know as _________, during which the dataset file is divided multiple smaller splits

processes each key-value pair as per the user-defined logic and further generates a key-value pair as its output

A _________________ is essentially a reducer function that groups a mapper's output locally, on the same node as the mapper

A reducer function can be used as combiner function or a custom user-defined function can be used

The mapReduce engine combines all values for a given key from the mapper output, creating multiple key-value pairs as input to the combiner where the key is not repeated and the value exists as a list of all corresponding values for that key

The ___________ stage is only an optimization stage, therefore it may not even be called by the mapReduce engine.

During this stage, if more than one reducer is involved, a partitioner divides the output from the mapper or combiner, if specified and called by the MapReducen engine, into partitions between reducer instances

The number of partitions equals the number of reduces

Is only an optimization stage, therefore it may not even be called by the mapReduce engine

Although each partition contains multiple key-value pairs all records for a particular key are within the same partition

The MapReduce engine guarantees a random and fair distribution between reducers while making sure that all of the same keys across multiple mappers end up with the same reducer instance

depending on the nature of the job, certain reducers can sometimes receive a large number of key-value pairs compared to others. As a result of this uneven workload, some reducers will finish earlier than others

Overall, this is less efficient and leads to longer job execution times than if the work was evenly split across reducers

this can be rectified by customizing the partitioning logic in order to guarantee a fair distribution of key-value pairs

This is the last stage of the map task

During the first stage of the reduce task, output from all partitioner is copied across the network to the nodes running the reduce task. This is known as _____________

Is the final stage of the reduce task

The list based key_value output from each partitioner can contains the same key multiple times

Next, The MapReducer engine automatically groups and sorts the key-value pairs according to the keys so that the output contains a sorted list of all input keys (and their values) with the same keys appearing together

To view the full output from the mapReduce job, all the file parts must be combined

The way keys are grouped and sorted can be customized

The MapReduce engine then merges each group of keys together before the shuffle and sort output is processed by the reducer function

This mere creates a single key-value pair per group, where key is the group key and the value is the list of all group values

The way kwys are grouped and sorted can be customized

Is the final stage of the reduce task

Depending on the user-defined logic specified in the reduce function (reducer), the reducer will either further summarize its input or will emit the output without making any changes

Thus, for each key-value pair that a reducer receives, the list of values stored in the value part of the pair is processed and another key-value pair is written out

The output key can either be the same as the input key or a substring value from the input value, or another serializable user-defined object

The output value can either be the same as the input value or a substring value from the input from the input value or another serializable user-defined object

__________________Just like the mapper, for the input key-value pair, a reducer may not produce any output key-value pair (filtering) or can generate multiple key-value pairs (demultiplexing)

Is essentially a reducer function that groups a mappers output locally, on the same node as the mapper

The output of the reducer, that is tyhe key-value pairs, is then written out as a separate file one file per reducer

To view the full output from the MapReduce job, all the file parts must be combined

the number of reducers can be customized

Different sub-datasets are spread across multiple nodes and are processed using the same algorthm

Refers to the parallelization of data processing by dividing a task into sub-tasks and running each sub-task on a separate processor, generally on a separate node in a cluster

Each sub-task generally executes a different algorithm, with its own copy of the same data or different data as its input, in parallel

Generally the output from multiple sub-tasks is joined together to obtain the final set of results

Refers to the parallelization of data processing by dividing a dataset into multiple sub-datasets and processing each sub-dataset in parallel

Each sub task generally executes a different algorithm wich its own copy of the same data or different data as its inputs, in parallel

Different sub-datasets are spread across multiple nodes and are pocessed using the same algorithm

Generally de output from each ptrocessed sub-dataset is joined together to obtain the final set of results

Within Big Data enviroments, the same task generally needs to be performed repeatdly on a data unit, such as a record, where the complete dataset is distributed across multiple locations due to its large size

MapReduce adresses this requirement by employing the data parallelism approach, where the data is divided into splits

Different sub-datasets are spread across multiple nodes and are processed using the same algorithm

Each split is then processed by its own instance of the map function, wich contains the same processing logic as the other map functions

The majority of traditional algorithmic development follows a sequential approach where operations on data are performed ona after the other in such a way that subsequent operations is dependent on its preceding operation
Here, operations are divided among the map and reduce functions

Map an Reduce task are independent, and in turn, run isolated from each other

Each instance of a map or reduce function runs independently of other instances

The logic within the reduce function is dependent on the output of the map function, in particular, which keys were emitted from the map function as the reduce function receives a unique key with a consolidated list of all of its values

Relatively simplistic algorithmic logic, such that he required result can be obtained bya pplying the same logic to different portions of a dataset in parallel, and then aggregating the results in some manner

requires a highky scalable processing engine that can be linearly scaled

Availability of the dataset in a distributed manner partitioned across a cluster so that multiple map functions can process diferent subset a datasets in parallel

understanding of the data structure within the dataset so that a meaningful data unit (a single record) can be chosen

Dividing algorithmic logic into map and reduce functions so that the logic in the map function is not dependent on the complete dataset, as only data within a single split is available

Emitting the correct key from the map function along with all the required data as value because the reduce function´s logic can only process those values that were emitted as part of the key-value pairs from the map function

Emiting the correct key from the reduce function along with the required data as value because the output from each reduce function becomes the final output of the mapReduce algorithm