Piotr Czarnas
Warszawa, Woj. Mazowieckie, Polska
37 tys. obserwujących
500+ kontaktów
37 tys. obserwujących
500+ kontaktów
Informacje
Do you trust the data that you use for data science?
Have you ever loaded invalid data…
Usługi
Aktywność
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Never postpone data quality. Data quality issues are too costly to fix later. When you are still building a data solution, any data quality issue is…
Never postpone data quality. Data quality issues are too costly to fix later. When you are still building a data solution, any data quality issue is…
Udostępnione przez: Piotr Czarnas
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Data profiling is both the most underrated and the most critical data quality practice. Data teams are always eager to start using new datasets…
Data profiling is both the most underrated and the most critical data quality practice. Data teams are always eager to start using new datasets…
Udostępnione przez: Piotr Czarnas
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Data quality monitoring for big data is all about the variety of data sets and their volume. Most of us, when we say "Big Data", we are thinking of…
Data quality monitoring for big data is all about the variety of data sets and their volume. Most of us, when we say "Big Data", we are thinking of…
Udostępnione przez: Piotr Czarnas
Doświadczenie
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Więcej działań użytkownika Piotr Czarnas
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Are you downloading all data to detect data quality issues? This is a question I have heard many times when someone wants to understand how to test…
Are you downloading all data to detect data quality issues? This is a question I have heard many times when someone wants to understand how to test…
Udostępnione przez: Piotr Czarnas
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Data quality monitoring is like an insurance policy. It is reducing the cost of failures. The list of possible data quality issues is extensive…
Data quality monitoring is like an insurance policy. It is reducing the cost of failures. The list of possible data quality issues is extensive…
Udostępnione przez: Piotr Czarnas
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Centralization is the primary reason why data quality projects fail. There is a tendency for data governance teams to set up and own a big data…
Centralization is the primary reason why data quality projects fail. There is a tendency for data governance teams to set up and own a big data…
Udostępnione przez: Piotr Czarnas
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The end-to-end data quality validation must combine data contract and business rules validation. A lot has been said about data contracts. They…
The end-to-end data quality validation must combine data contract and business rules validation. A lot has been said about data contracts. They…
Udostępnione przez: Piotr Czarnas
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If you failed to implement a data quality management process, it's likely because you did not follow the best practices. A successful data quality…
If you failed to implement a data quality management process, it's likely because you did not follow the best practices. A successful data quality…
Udostępnione przez: Piotr Czarnas
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Business data stewards don't know how to test data quality. They are great data owners, but struggle with technical tasks. Data engineers don't want…
Business data stewards don't know how to test data quality. They are great data owners, but struggle with technical tasks. Data engineers don't want…
Udostępnione przez: Piotr Czarnas
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Did you ever fail to implement data quality management? Most teams have failed because they wanted to start big. It is not the right approach. We…
Did you ever fail to implement data quality management? Most teams have failed because they wanted to start big. It is not the right approach. We…
Udostępnione przez: Piotr Czarnas
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Do you know why data observability is so popular? Because it enables an agile approach to data quality. Instead of testing the quality of all…
Do you know why data observability is so popular? Because it enables an agile approach to data quality. Instead of testing the quality of all…
Udostępnione przez: Piotr Czarnas
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Data quality is all about validation rules. Is your data usable? Check it with data quality rules. The good news is that you don't have to define…
Data quality is all about validation rules. Is your data usable? Check it with data quality rules. The good news is that you don't have to define…
Udostępnione przez: Piotr Czarnas
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Data Governance without Data Quality makes no sense. The purpose of governance is to establish order. The order must be measured. A data quality…
Data Governance without Data Quality makes no sense. The purpose of governance is to establish order. The order must be measured. A data quality…
Udostępnione przez: Piotr Czarnas
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Everybody wants clean data, but it requires a little effort to get it. We have two options: accept only clean data or clean (enrich) it. The first…
Everybody wants clean data, but it requires a little effort to get it. We have two options: accept only clean data or clean (enrich) it. The first…
Udostępnione przez: Piotr Czarnas
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We should not forget that the lifecycle of a data platform does not end when it is deployed. It starts when it is deployed! The usefulness and…
We should not forget that the lifecycle of a data platform does not end when it is deployed. It starts when it is deployed! The usefulness and…
Udostępnione przez: Piotr Czarnas
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Data governance is the unsung hero of AI success. Overlooked when it works, blamed when it falters. Here are 10 harsh truths of data governance, the…
Data governance is the unsung hero of AI success. Overlooked when it works, blamed when it falters. Here are 10 harsh truths of data governance, the…
Polecane przez: Piotr Czarnas
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Each data platform should guarantee the quality of its data. That is the purpose of data contracts: to define the schema and constraints. As shown…
Each data platform should guarantee the quality of its data. That is the purpose of data contracts: to define the schema and constraints. As shown…
Udostępnione przez: Piotr Czarnas