Top Leading Technologies for Cutting-Edge Data Processing Systems

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Welcome To Loginworks Softwares
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Top Leading Technologies for Cutting-Edge Data
Processing Systems
Data processing technologies are developing as
rapidly as data collection is advancing, that is,
at a continually accelerating rate. Theres a
whole lot of technology that is breaking ground
and offering new solutions in this exciting
field. Lets take a look at what some of the
latest cutting-edge technologies are for data
processing systems.
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Distributed Systems Architecture
Big data sets common in data processing today
have limitations on computational power. The
technology needed to deal with this is called
distributed systems architecture.
MPP Massive parallel processing, and Hadoop
are two key technologies that are leading the
industry in distributed systems architecture.
Both feature the shared nothing technology that
ensures autonomous operation. The key difference
between the two is that MPP is proprietary and
rather costly to implement, while Hadoop is open
source and can be integrated from very small, low
cost applications, to very very large ones. While
Hadoop is more recent than MPP, and allows
flexibility and scalability, MPP remains slightly
quicker. MPP systems are provided by Teradata, Ne
tezza, Vertica, and Greenplum. Oracle and
Microsoft also have their own MPP systems.
Hadoop is a software project by Apache,
containing a collection of software utilities
that provide huge storage and processing power.
Hadoop uses MapReduce to process large
non-structured data sets, as the name implies, by
a map function, and a reduce function within
Hadoop. Many platforms can be built on top of the
Hadoop framework. Non-proprietary applications
available for use on Hadoop continue to develop
in number and complexity.
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Query Optimization Part of leading technology for
data processing in a relational database is
query optimization design. Query optimization is
an automated process that attempts to provide the
best possible answers based on a range of
possible query plans. A query plan is a set of
rules that a relational database uses to search
data for the required parameters. Query
optimization can effectively determine which
searches are valid, and which will be most
accurate, efficient, and timely.
Query hints may be built into query
optimization, for example, a query on a GPS
database might be selected for the fastest or the
quickest route. A simplified example of query
optimization is to imagine a query for the number
of a certain car make and model, where the
database could search all makes then all models,
just all models, since the model subset
automatically includes make. Query optimization
would choose the latter.
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Non-Relational databases No-SQL
With the explosion of Big-Data, has come two
more players in data processing technology, non
structured and dark data. Traditional databases h
ave relational structure, usually called
relational data base management systems (RDBMS),
and are primarily built on SQL structured query
language, which is why non-relationship databases
are coined No-SQL. A Non-relational, No-SQL datab
ase can store and access un-structured data
easily using a common data format called JSON
documents, and can import JSON, CSV, and TSV
formats. A JSON, Javascript Object Notation is a
lightweight data-interchange format, simple yet
very powerful, since stored data need not be
structured. The ability to store and access this
non-structured data is what makes non-relational
databases such important technology for data
analytics systems. As a draw back, since they are
non relational, the query itself has to draw a
relation, so working with a non-relational
database requires more skill. Popular No-SQL data
bases used in data processing are MongoDB, Arango
DB, Apache Ignite, and Cassandra.
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Data Virtualization Data storage and retrieval ca
n sometimes deteriorate data due to the format
that is required by the storage or retrieval.
Unlike the traditional ETL (extract, transform,
load) data method, in data virtualization the
data remains where it is, a viewer accesses it in
real time, from its existing location, solving
the problem of format losses. An abstraction
layer between viewer and source means that the
data can be used without extraction and
transformation. A simplified example of data virt
ualization we can all identify with is the
technology that drives images on social media.
When you view an image on most social media
platforms, normally youre viewing it temporarily
in real time on your mobile device or computer,
but it exists in reality on the server of
whichever social media youre on. The file format
is not relevant, nor do you need software related
to the format to view it. The image is only
converted into real data if its downloaded or
via a screenshot, but the data is searchable and
viewable without ever opening the file itself
because of data virtualization.
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Stream Processing and Stream Analytics
Stream processing provides the capability for
performing actions and analyzing events on
real-time data. To do this stream processing
makes use of a series of continuous queries.
Stream processing allows data information to be
processed before it lands in a database, which
makes it incredibly powerful. A good example to e
xplain the process of live stream data analytics
is the correlation of GPS data or driver mobile
data with user locations. Ubers apps have used
this with great success to revolutionize private
transport. Many bank applications also use stream
processing to immediately alert users of
suspicious activity. Striim, IBM Infosphere, SQLS
tream, and Apache Spark are examples of common
streaming database applications.
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Data Mining and Scraping Data mining and scraping
technology is improving the content that
data-processing systems have available in the
data capture phase. Data mining in its simplest
form essentially takes very large sets of data
and extracts smaller more useful sets. Data
mining software automizes the fundamental data
processing function of finding patterns in large
data sets, to create smaller subsets which match
search query criteria. Web search is essentially
a form of data mining we all use, taking the
catalogue of websites and extracting only those
that match search terms. Data mining may be
applied to any type of data, text, audio, video,
images. Data mining can be incredibly useful in
finding information a company doesnt currently
have from large unstructured data sources.
Scraping is similar to mining, but where mining
analyzes data for patterns, scraping collects
data matching certain parameters.
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Machine Learning and AI Data processing is a key
field for advances in machine learning and AI.
Data preparation involves cleaning and
transforming the data for us. It often takes
around 60 to 80 of the whole data processing
time, with as little as 20 for analytics and
presentation. The preparation of data is largely
repetitive and time consuming, so it is a perfect
area for implementation of the latest technology
in machine learning. Processing large amounts of
data, especially when complex text based data
like searching contracts, reports, articles,
machine learning is a one of the latest
technological advancements that will improve the
industry. Machine learning can match phrases in a
range of documents based on connections that
previously only humans could do. We think of AI
and machine learning as way out there, but we
actually interact with it every day on platforms
like Google search. Havent you noticed how it
seems to know more and more what you might be
thinking, with scary accuracy? Its a simple
concept yet, currently one of the most extensive
examples of machine learning data processing in
everyday use. Machine learning is also growing
steadily in user interaction devices on the web.
Automated answers to users questions, along with
databasing questions and responses for improved
machine learning, helps organizations better
serve their customers. AI and machine intelligenc
e is advancing faster than we can train people to
work with it. An unbelievable 2 jobs are
available for every AI graduate in the UK.
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Data Compression Compression is driving data proc
essing, with larger and larger data sets, any
reduction in data sizes will improve experiences.
Storage space and processing times can be reduced
significantly with better compressions
techniques, this in turn significantly reduces
costs and improves performance. Facebook has
released their latest compression tool Z standard
on an open source platform. While previous
storage compression devices had around 9 levels,
Z standard has 22 levels. Data compression will
help improve our storage and processing
capacities. Self-Driving Database Management Syst
ems The last and most significant technology in d
ata processing systems is the self-driving
database management system. A self-driving
database can be run without user intervention,
and totally managed by the user. Leading this
technological advancement is Oracles Autonomous
Database. Oracles founder claims it will
revolutionize data management, since there is no
need to apply patches, complete manual back-ups,
or tune, its capable of total automation.
Peleton is a good example of a leading open
source autonomous database solution.
For data processing, its important to stay
ahead of the trends. Check out some of the ideas
weve discussed here to find out more about where
your data processing systems can evolve.
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