Extreme data growth, changing data types and distribution of data set almost impossible requests to existing traditional data bases. Data base systems are growing more and more complex without actually providing better usage models or increasing business value. Fast and simple access to data of any type and in any location is paramount for extracting real value from data. New data base systems come with shift in fundamental design and possibilities and still provide well know and established mechanisms for data access and integration.

1. The future of database
and some other smaltalk
Renato Telen

3. A brief history of databases

4. The pioneers of DB

6. The very beginning

7. First integrations

8. Arrival of analytics

14. Augmented analytics
Using machine learning and AI, augmented analytics is
considered as a disrupter in the data and analytics market
because it will transform how analytics content is
developed, consumed and shared.

15. Augmented data management
Augmented data management utilises machine learning
capabilities and AI technology to make data management
categories including data quality, master data
management, metadata management, data integration as
well as database management systems (DBMSs) self-
configuring and self-tuning.

16. Continous intelligence
Continuous data is more than a new way to say real-time
data. Instead, it’s about a design pattern where real-time
analytics are combined with business operations,
processing current and historical data to prescribe actions
in response to events.

17. Explainable AI
AI is already being increasingly used in data management, but
how do AI solutions explain why they came to certain
conclusions? This is where explainable AI comes in.
Explainable AI in data science and ML platforms is about
generating an explanation of data models in terms of accuracy,
attributes, model statistics and features in natural language.

18. Graph
Graph analytics is a set of analytic techniques that help
enterprises explore the relationships between entities of interest
such as transactions, processes and staff.
The application of graph processing and graph database
management systems will grow at 100% annually through 2022.

19. Data fabric
Data fabric is all about a single and consistent data management
framework. It looks at enabling frictionless access and sharing of
data in a distributed data environment as opposed to siloed
storage.

20. NLP/ conversational analytics
By 2023, 50% of analytical queries will be generated via search,
natural language processing (NLP) or voice, or will be
automatically generated. The need to analyse complex
combinations of data and to make analytics accessible to everyone
in the organisation will drive broader adoption, allowing analytics
tools to be as easy as a search interface or a conversation with a
virtual assistant.

21. Commercial AI and ML
By 2023, 75% of new end-user solutions leveraging ML and AI
techniques will be built with commercial solutions rather than
open source platforms.
Commercial vendors have created connectors into the Open
Source ecosystem, and they provide organisations with features
necessary to scale and democratise AI and ML such as project
and model management, transparency, reuse, data lineage,
platform cohesiveness and integration that Open Source
technologies lack.

22. Ledger technologies
Distributed ledger technologies such as blockchain are
looking promising in the area of data analytics due to the
possibility that they provide decentralised trust across a
network of untrusted participants.