HRDC_SEMINAR_Database management systems Today

Google Cloud SQL

 It’s a wholly managed database service on the cloud that
allows users to choose their preferred database server.

 Using this takes away some of the critical burdens faced by in
house hosting strategies like:

 High Availability: to ensure that databases are up and functioning
24/7, cloud SQL provides automated failover strategies.

 High Security: Provides encryption that aligns with industry-standard
compliances.

 Multiple Connectivity Services – Google Cloud SQL provides
multiple connection endpoints to the database server. For example, you
can connect using a public IP address with SSL, or a JDBC library socket,
or even the cloud shell.

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AI in Database Management

 One of the trends today is to leverage AI to automate small
independent processes to improve database performance.

 Where can AI in database be used?

 SQL query optimization
 Identifies the slow queries and recommend optimization

processes(applies data analytics).

 A large amount of public data is a useful foundational dataset to
train machine learning algorithms to identify patterns.

 These models are trained to identify queries that are considered
slow and provide recommendations to improve query retrieval
speed.

 Database AI allows organizations to save both cost and time.

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Augmented Database Management

 At the end of 2022, data management manual tasks will be reduced by
45 percent through the addition of ML and automated service-level
management.

 As organizations are becoming more dynamic and widely spread
across multiple functions, it has become essential to deliver agile
solutions.

 To adapt and keep database management simpler, tasks such as Data
Quality and Metadata Management will be automated with augmented
data management.

 Augmented data management allows you to perform tasks like schema
recognition, regulatory compliance, and utilization with ease.

 Furthermore, augmented data management will allow databases to be
self-tuning and correcting with the help of AI and machine learning
analysis.

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Example

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Graph database

 Consider the sophisticated business cases such as digital twins in
IoT and natural language analysis.

 How to build some relationships between these unstructured data
groups?

 A graph database provides features to store and relate unstructured
data at scale.

 It also provides interfaces to query information bearing the
relationship in mind. It allows for complicated relationships called
graphs to be built and maintained in a single place.

 Neo4J is leading this trend by providing advanced features such
as data modeling paths and data design scenarios.

 It provided a framework to build data and retrieve them in the
best possible manner.

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Relational model to graph Model conversion

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Big Data

 Big data does not necessarily mean lots of data.
 It is the ability to process any type of data such as semi-

structured, unstructured data or structured data.
 It enables organizations to gather key insights and patterns

from the available data and helps them in taking intelligent
decisions based on the same.

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https://www.researchgate.net/figure/Representation-of-the-five-Vs-of-big- 18-06-2022
58 data_fig2_338166638

Influence of Big data and cloud in
converged architectures

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 There are several types of NoSQL Databases and tools
available to store and process the Big Data.

 NoSQL Databases are optimized for data analytics using the
BigData such as text, images, logos, and other data formats
such as XML, JSON.

 The big data is helpful for developing data-driven intelligent
applications.

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An Increased focus on Security

 The rising number of cyberattacks clearly highlights the increased need for data
security.

 Firewalls and an antivirus can no more be the only solution for prevention of data
loss.

 To eliminate potential internal weaknesses such as issues related to network
privileges, even hardware or software misconfigurations that could be misused,
resulting in data leaks.

 How can you effectively implement these trends within your organization?

 GDPR-General Data protection Regulation

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Current landscape for database

management systems

 Most of the organizations run multiple DBMSs

 Polyglot persistence-

 using the right database platform for each specific requirement, rather than
trying to force fit everything into a single DBMS.

 Multiple relational model
 Pre-relational

 An organization might have an Oracle, SQL Server, MySQL, PostgreSQL,
and so on, and on top of that, there are still organizations, usually larger
organizations, running mainframe systems that use pre-relational databases,
such as IMS(Information Management System) or IDMS( Integrated
Database Management System,).

 No SQL

 Document
 Graph
 Key/value
 Wide column store
 Hybrid

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 Hybrid transactional analytic processing(HTAP)

 using a single DBMS to deliver both transaction processing and
analytics workloads.

 Not just DBMS, also Hadoop and spark
 Commercial and open source

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CASE STUDY

 Google Cloud Spanner

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CASE STUDY

 Google Cloud Spanner

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