Saturday, January 30, 2021

Digital Transformation (18.1. - 24.1.2021)

Five trends expected to be seen this year in the world of digital transformation

Three different perspectives in data modeling

What is threatening data integrity in your company and what to do

Ensuring data quality with effective data management

Data democratization is not just about access to data, but also about data literacy 

Evolution of data catalogs and where we are now

What is a data hub and its distinction from data lakes and data warehouses

Six BI artifact types and when they are useful

No-code application development can help in digital transformation initiatives, with properly defined boundaries from IT

Which of four strategies for implementing AI in your company you should choose?

Don't hire data scientists to fill all roles related to the ML environment 

And you should start to care more about MLOps

Friday, January 29, 2021

IT links (18.1. - 24.1.2021)

JEP draft: Primitive objects

Testing logging output in Java with System Stubs

Reminding: Spring injection types

How to analyze Java thread dumps

Writing clean architecture with Spring Boot

A new possibility how to run Java programs with new GraalVM 21.0

It is already 25 years from the first release of Java

And IntelliJ IDEA turns 20 years these days

Google Cloud Platform is the best overall cloud provider acording to Cockroach Labs annual cloud report

You shouldn't reproduce issues in remote environments - 5 reasons

Wednesday, January 27, 2021

AI, ML, Robots and Brains (11.1. - 17.1.2021)

Society


Algorithms



Health


Robots


Cars


Hardware


Brains

Saturday, January 23, 2021

Digital Transformation (11.1. - 17.1.2021)

Five data governance trends in 2021

How to create a data strategy? Three main points and what you need for it

One webinar about data strategy - Plans are useless, but planning is invaluable

And how to improve data strategy in 2021?

DataOps, MLOps, AIOps - How to choose between them?

And what is DataOps exactly?

Three types of data mesh

Why to use cloud data warehouse and how to migrate your company data?

Combining a data lake and data warehouse to create a hybrid data lake

Building cloud data lakes - challenges and lessons learned

Designing data pipelines

Data deduplication - reducing costs and relation to cloud usage

Reducing data downtime and improving data quality with data observability

Overview of data related roles

Hiring people for the data team

Autonomous economic agents in decentralized autonomous organizations in M2M economy

Tuesday, January 19, 2021

AI, ML, Robots and Brains (4.1. - 10.1.2021)

Society


Algorithms


Health


Robots


Cars


Hardware


Brains

Thursday, January 14, 2021

Digital Transformation (4.1. - 10.1.2021)

What is business inteligence and how it differs from business analytics?

Data testing only is not going to prevent broken data pipelines - data monitoring and observability is needed too.

Building a data analytics team + technical assets and capabilities that are needed for it.

And six tips how to build a data science team at a small company.

Data scientists will need more and more data engineering skills in near future - here are 5 reasons.

Monday, January 11, 2021

Digital Transformation (28.12.2020 - 3.1.2021)

Data mobility and cloud services are going to be essential in 2021. Also, understanding unstructured Data Management can be crucial.

What are other upcoming trends in data science that will affect business strategies?

The evolution of big data compute platforms - from on-premises clusters to serverless data pipelines using microservices.

How modern unified data architecture could look like? 

Selecting tools that promote good data quality (data quality monitoring systems) from the first principles - even small data quality issues can indicate broken business processes.

Building high performing data science teams. And how to test a new data scientist candidate

Why is it so hard to build products based on data science and machine learning? 

7 MLOps smells that might indicate deeper problems. 

And why we should stop using the black-box metaphor in machine learning?

Thursday, January 7, 2021

IT links (28.12.2020 - 3.1.2021)

Small changes in JDK 16 specifications, the biggest one in pattern matching functionality

Why and when to use Java for work with big data?

The year 2020 in Spring

Querying a table in one database from another in PostgreSQL 

Vulnture - internal security vulnerability reporting tool made by Airbnb, available on GitHub

I really like Linux screen command, it is very useful, mainly when you want to run multiple parallel processes at one time

Team-level Agile anti-patterns - hmm, I recognize some of them from my own experience ...

Monday, January 4, 2021

AI, ML, Robots and Brains (21.12. - 27.12.2020)

Society


Algorithms


Health


Robots


Cars


Hardware


Brains

Saturday, January 2, 2021

Digital Transformation (21.12. - 27.12.2020)

Three important things for implementation of data management strategies in a company

Five mistakes to avoid when building a data analytics platform

What is expected to come in 2021 in field of data collection

A review of two data ingestion tools - comparison, advantages, disadvantages

Overcoming data scarcity and privacy challenges - with synthetic data - when, why to use it

Metadata is useless - if not applied - application is everything

Improving data warehouse quality - with quality checks (helping to detect bugs in ETL pipelines, etc.), improving data consistency etc. 

Typical technical debts within data warehouses and data lakes and how to fight them

Netflix way of optimizing data warehouse storage

What is feature store and how it differs from data warehouse?

AutoML tools can be really useful, but you should know how to analyze performance of generated models etc.

Enterprise machine learning challenges and strategies for 2021