Since the beginning of my journey into the data world I’ve been keen on making professionals better at their data job. In the previous years that took the shape of creating materials in several different forms that could help people understand, use, and avoid mistakes on their data tool of choice. But now there’s much more into it: a trusted AI solution to help data professional in their day to day optimization job.

Everyone is now talking about AI, and modern databases like PostgreSQL® are increasingly being adopted in companies’ AI journey as sources of data or key pieces of the AI infrastructure. Moreover there’s a new set projects that are solving PostgreSQL problems with AI.

You can query a JSON document in MySQL to find content within it with:

• The JSON_CONTAINS function that will return if a JSON document is contained within another JSON document. The function returns 1 if the document is contained, 0 elsewhere.
• The JSON_CONTAINS function that will return if a JSON document path is contained within another JSON document. The function returns 1 if the document is contained, 0 elsewhere.

One of the most common tasks with strings is concatenation! This blog post showcases several techniques to perform it with MySQL.

Earlier this month I wrote a piece on solving Gunnar Morling interesting 1 billion rows challenge in PostgreSQL and ClickHouse. Since Aiven provides also MySQL, I wanted to give it a try. TLDR; The results are much slower than PG and ClickHouse, do you have any suggestion on how to improve?

You can tabulate a JSON document (retrieve it as a row) in MySQL with the JSON_TABLE function.

You can remove a field from JSON document in MySQL with the JSON_REMOVE function.

You can insert a field in a JSON document in MySQL with the JSON_INSERT function.

You can edit a JSON document in MySQL with:

• The JSON_SET function that will replace values for JSON paths that exists and add values for the ones that don’t exist.
• The JSON_REPLACE function that will replace values for JSON paths that exists and ignore the ones that don’t exist.

You can merge two JSON documents in MySQL with:

• the JSON_MERGE_PRESERVE function to concatenate the document values
• the JSON_MERGE_PATCH function to keep the latest value for each key

You can create a JSON document from fields in Mysql® with the JSON_OBJECT function

To get the type of a JSON item in MySQL you need to use the JSON_TYPE function.

To extract an item from an array in MySQL you need to use the -> operator and the [item_number] JSON Path Syntax.

MySQL® offers three ways to extract fields from a JSON object:

• the -> operator to extract the field as JSON
• the ->> operator to extract the field as text
• the JSON_EXTRACT function

Both operators use the JSON Path Syntax

To load JSON data into a MySQL column you need to include it as string.

This series covers how to solve common problems on JSON datasets with MySQL and it includes (links will appear once the target pages are up):

Since the beginning of my journey into the data world I’ve been keen on making professionals better at their data job. In the previous years that took the shape of creating materials in several different forms that could help people understand, use, and avoid mistakes on their data tool of choice. But now there’s much more into it: a trusted AI solution to help data professional in their day to day optimization job.

Testing a database performance is a must in every company. Despite everyone’s needs beings slightly different, a good starting point for PostgreSQL® database is using pgbench: a tool shipped with the PostgreSQL installation that allows you to stress test a local or remote database. This blog post showcases how to install (on a Mac) and use pgbench to create load on a remote PostgreSQL database on Aiven.

Since the beginning of my journey into the data world I’ve been keen on making professionals better at their data job. In the previous years that took the shape of creating materials in several different forms that could help people understand, use, and avoid mistakes on their data tool of choice. But now there’s much more into it: a trusted AI solution to help data professional in their day to day optimization job.

Testing a database performance is a must in every company. Despite everyone’s needs beings slightly different, a good starting point for PostgreSQL® database is using pgbench: a tool shipped with the PostgreSQL installation that allows you to stress test a local or remote database. This blog post showcases how to install (on a Mac) and use pgbench to create load on a remote PostgreSQL database on Aiven.

Some time ago I found an interesting database file suffix I never faced before: the .dbf and saw around that it was first introduced in 1983 with dBASE II. This article showcases how we can automatically generate the PostgreSQL table and fill it with data using Python and dbfread.

Everyone is now talking about AI, and modern databases like PostgreSQL® are increasingly being adopted in companies’ AI journey as sources of data or key pieces of the AI infrastructure. Moreover there’s a new set projects that are solving PostgreSQL problems with AI.

A boolean value should only contain two values, True or False, but is it correct? Usually people assume so, but sometimes miss the fact that there could be the absence of the value all-together. In databases this is absence is usually stored as NULL and this blog showcases how to find them, use them properly and 11 lessons to learn to be a NULL Pro!

Keep in mind, it’s not only booleans that can contain NULL values, it’s all the columns where you don’t define a NOT NULL constraint!

You have a JSON dataset that you want to upload to a PostgreSQL table containing properly formatted rows and columns… How do you do it?

All the main sources like my own blog and others tell you to load the JSON in a dedicated temporary table containing a unique JSON column, then parse it and load into the target table. However there could be another way, avoiding the temp table!

How to load StackOverflow StackExchange data in a PostgreSQL® database!

Need to extract a specific substring out of a text in PostgreSQL®? Read here how!

Last week the good old Gunnar Morling launched an interesting challenge about ordering 1 billion rows in Java. Like my ex colleague and friend Robin Moffat, I’m not at all a Java expert, and while Robin used DuckDB to solve the challenge, I did the same with PostgreSQL and ClickHouse.

PostgreSQL® offers two types of data types to handle JSON data, JSON and JSONB. This doc showcases how to load JSON data into a column.

PostgreSQL® offers two types of data types to handle JSON data, JSON and JSONB, and provides two different ways to tabulate a JSON to a record:

PostgreSQL® offers two types of data types to handle JSON data, JSON and JSONB, and provides two different ways to tabulate a JSON to a recordset:

PostgreSQL® offers two types of data types to handle JSON data, JSON and JSONB, you can use the jsonb_set function to remove the null values for JSONB columns.

PostgreSQL® offers two types of data types to handle JSON data, JSON and JSONB, you can use the - operator to remove an item from an array.

PostgreSQL® offers two types of data types to handle JSON data, JSON and JSONB, and provides two different ways to remove fields from a JSON document:

PostgreSQL® offers two types of data types to handle JSON data, JSON and JSONB, you can use the json_strip_nulls (jsonb_strip_nulls for JSONB) function to remove the null values.

PostgreSQL® offers two types of data types to handle JSON data, JSON and JSONB, you can use the opearator || to concatenate two JSON objects.

PostgreSQL® offers two types of data types to handle JSON data, JSON and JSONB, you can use the function json_object (jsonb_object for JSONB) to create a JSON object from an array of key/value pairs.

PostgreSQL® offers two types of data types to handle JSON data, JSON and JSONB, you can use the function json_object (jsonb_object for JSONB) to create a JSON object from two arrays of keys and values.

PostgreSQL® offers two types of data types to handle JSON data, JSON and JSONB, you can use the function array_to_json (array_to_jsonb for JSONB) to create a JSON array from an existing array.

PostgreSQL® offers two types of data types to handle JSON data, JSON and JSONB, you can use the function json_build_array (jsonb_build_array for JSONB) to create a JSON array from a list of elements.

PostgreSQL® offers two types of data types to handle JSON data, JSON and JSONB, you can use several functions to extract the keys and related values in tabular format.

PostgreSQL® offers two types of data types to handle JSON data, JSON and JSONB, you can use the function to_json (to_jsonb for JSONB) to convert a table row to a JSON object.

PostgreSQL® offers two types of data types to handle JSON data, JSON and JSONB, you can use the function json_pretty (jsonb_pretty for JSONB) to prettify the output.

PostgreSQL® offers two types of data types to handle JSON data, JSON and JSONB, and provides several different ways to parse arrays from a JSON document:

This series covers how to solve common problems on JSON datasets with PostgreSQL® and it includes (links will appear once the target pages are up):

PostgreSQL® offers two types of data types to handle JSON data, JSON and JSONB, and provides several different ways to extract fields from a JSON document:

PostgreSQL® offers two types of data types to handle JSON data, JSON and JSONB, you can use the function json_typeof (jsonb_typeof for JSONB) to extract the fields type.

PostgreSQL® offers two types of data types to handle JSON data, JSON and JSONB. This doc showcases how to index a JSONB column with a GIN index.

PostgreSQL® offers two types of data types to handle JSON data, JSON and JSONB, and provides several different ways to check if a value/field from a JSON document:

PostgreSQL® offers two types of data types to handle JSON data:

• JSON stores the JSON as text, performing a validation on the correctness of the JSON syntax
• JSONB optimizes the JSON storage in a custom binary format. Therefore, on top of validating the correctness of the JSON format, time is spent to properly parse and store the content.

I started following the #PGSQLPhriday initiative a couple months back but never had the time to properly sit down and write due to conference traveling. Therefore I was super happy to be at home this week and find out that PGSQL Phriday #003 theme is about the PostgreSQL community!

How to define a database connection in a JSON file that can be imported in PG Admin 4

Testing a database performance is a must in every company. Despite everyone’s needs beings slightly different, a good starting point for PostgreSQL® database is using pgbench: a tool shipped with the PostgreSQL installation that allows you to stress test a local or remote database. This blog post showcases how to install (on a Mac) and use pgbench to create load on a remote PostgreSQL database on Aiven.

Some time ago I found an interesting database file suffix I never faced before: the .dbf and saw around that it was first introduced in 1983 with dBASE II. This article showcases how we can automatically generate the PostgreSQL table and fill it with data using Python and dbfread.

Some time ago I found an interesting database file suffix I never faced before: the .dbf and saw around that it was first introduced in 1983 with dBASE II. This article showcases how we can automatically generate the PostgreSQL table and fill it with data using Python and dbfread.

Some time ago I found an interesting database file suffix I never faced before: the .dbf and saw around that it was first introduced in 1983 with dBASE II. This article showcases how we can automatically generate the PostgreSQL table and fill it with data using Python and dbfread.

A boolean value should only contain two values, True or False, but is it correct? Usually people assume so, but sometimes miss the fact that there could be the absence of the value all-together. In databases this is absence is usually stored as NULL and this blog showcases how to find them, use them properly and 11 lessons to learn to be a NULL Pro!

Keep in mind, it’s not only booleans that can contain NULL values, it’s all the columns where you don’t define a NOT NULL constraint!

You can query a JSON document in MySQL to find content within it with:

• The JSON_CONTAINS function that will return if a JSON document is contained within another JSON document. The function returns 1 if the document is contained, 0 elsewhere.
• The JSON_CONTAINS function that will return if a JSON document path is contained within another JSON document. The function returns 1 if the document is contained, 0 elsewhere.

PostgreSQL® offers two types of data types to handle JSON data, JSON and JSONB, and provides several different ways to check if a value/field from a JSON document:

You can query a JSON document in MySQL to find content within it with:

• The JSON_CONTAINS function that will return if a JSON document is contained within another JSON document. The function returns 1 if the document is contained, 0 elsewhere.
• The JSON_CONTAINS function that will return if a JSON document path is contained within another JSON document. The function returns 1 if the document is contained, 0 elsewhere.

You have a JSON dataset that you want to upload to a PostgreSQL table containing properly formatted rows and columns… How do you do it?

All the main sources like my own blog and others tell you to load the JSON in a dedicated temporary table containing a unique JSON column, then parse it and load into the target table. However there could be another way, avoiding the temp table!

You can tabulate a JSON document (retrieve it as a row) in MySQL with the JSON_TABLE function.

You can remove a field from JSON document in MySQL with the JSON_REMOVE function.

You can insert a field in a JSON document in MySQL with the JSON_INSERT function.

You can edit a JSON document in MySQL with:

• The JSON_SET function that will replace values for JSON paths that exists and add values for the ones that don’t exist.
• The JSON_REPLACE function that will replace values for JSON paths that exists and ignore the ones that don’t exist.

You can merge two JSON documents in MySQL with:

• the JSON_MERGE_PRESERVE function to concatenate the document values
• the JSON_MERGE_PATCH function to keep the latest value for each key

You can create a JSON document from fields in Mysql® with the JSON_OBJECT function

To get the type of a JSON item in MySQL you need to use the JSON_TYPE function.

To extract an item from an array in MySQL you need to use the -> operator and the [item_number] JSON Path Syntax.

MySQL® offers three ways to extract fields from a JSON object:

• the -> operator to extract the field as JSON
• the ->> operator to extract the field as text
• the JSON_EXTRACT function

Both operators use the JSON Path Syntax

To load JSON data into a MySQL column you need to include it as string.

This series covers how to solve common problems on JSON datasets with MySQL and it includes (links will appear once the target pages are up):

PostgreSQL® offers two types of data types to handle JSON data, JSON and JSONB. This doc showcases how to load JSON data into a column.

PostgreSQL® offers two types of data types to handle JSON data, JSON and JSONB, and provides two different ways to tabulate a JSON to a record:

PostgreSQL® offers two types of data types to handle JSON data, JSON and JSONB, and provides two different ways to tabulate a JSON to a recordset:

PostgreSQL® offers two types of data types to handle JSON data, JSON and JSONB, you can use the jsonb_set function to remove the null values for JSONB columns.

PostgreSQL® offers two types of data types to handle JSON data, JSON and JSONB, you can use the - operator to remove an item from an array.

PostgreSQL® offers two types of data types to handle JSON data, JSON and JSONB, and provides two different ways to remove fields from a JSON document:

PostgreSQL® offers two types of data types to handle JSON data, JSON and JSONB, you can use the json_strip_nulls (jsonb_strip_nulls for JSONB) function to remove the null values.

PostgreSQL® offers two types of data types to handle JSON data, JSON and JSONB, you can use the opearator || to concatenate two JSON objects.

PostgreSQL® offers two types of data types to handle JSON data, JSON and JSONB, you can use the function json_object (jsonb_object for JSONB) to create a JSON object from an array of key/value pairs.

PostgreSQL® offers two types of data types to handle JSON data, JSON and JSONB, you can use the function json_object (jsonb_object for JSONB) to create a JSON object from two arrays of keys and values.

PostgreSQL® offers two types of data types to handle JSON data, JSON and JSONB, you can use the function array_to_json (array_to_jsonb for JSONB) to create a JSON array from an existing array.

PostgreSQL® offers two types of data types to handle JSON data, JSON and JSONB, you can use the function json_build_array (jsonb_build_array for JSONB) to create a JSON array from a list of elements.

PostgreSQL® offers two types of data types to handle JSON data, JSON and JSONB, you can use several functions to extract the keys and related values in tabular format.

PostgreSQL® offers two types of data types to handle JSON data, JSON and JSONB, you can use the function to_json (to_jsonb for JSONB) to convert a table row to a JSON object.

PostgreSQL® offers two types of data types to handle JSON data, JSON and JSONB, you can use the function json_pretty (jsonb_pretty for JSONB) to prettify the output.

PostgreSQL® offers two types of data types to handle JSON data, JSON and JSONB, and provides several different ways to parse arrays from a JSON document:

This series covers how to solve common problems on JSON datasets with PostgreSQL® and it includes (links will appear once the target pages are up):

PostgreSQL® offers two types of data types to handle JSON data, JSON and JSONB, and provides several different ways to extract fields from a JSON document:

PostgreSQL® offers two types of data types to handle JSON data, JSON and JSONB, you can use the function json_typeof (jsonb_typeof for JSONB) to extract the fields type.

PostgreSQL® offers two types of data types to handle JSON data, JSON and JSONB. This doc showcases how to index a JSONB column with a GIN index.

PostgreSQL® offers two types of data types to handle JSON data, JSON and JSONB, and provides several different ways to check if a value/field from a JSON document:

PostgreSQL® offers two types of data types to handle JSON data:

• JSON stores the JSON as text, performing a validation on the correctness of the JSON syntax
• JSONB optimizes the JSON storage in a custom binary format. Therefore, on top of validating the correctness of the JSON format, time is spent to properly parse and store the content.

How to define a database connection in a JSON file that can be imported in PG Admin 4

You can query a JSON document in MySQL to find content within it with:

• The JSON_CONTAINS function that will return if a JSON document is contained within another JSON document. The function returns 1 if the document is contained, 0 elsewhere.
• The JSON_CONTAINS function that will return if a JSON document path is contained within another JSON document. The function returns 1 if the document is contained, 0 elsewhere.

You can query a JSON document in MySQL to find content within it with:

• The JSON_CONTAINS function that will return if a JSON document is contained within another JSON document. The function returns 1 if the document is contained, 0 elsewhere.
• The JSON_CONTAINS function that will return if a JSON document path is contained within another JSON document. The function returns 1 if the document is contained, 0 elsewhere.

Everyone is now talking about AI, and modern databases like PostgreSQL® are increasingly being adopted in companies’ AI journey as sources of data or key pieces of the AI infrastructure. Moreover there’s a new set projects that are solving PostgreSQL problems with AI.

Everyone is now talking about AI, and modern databases like PostgreSQL® are increasingly being adopted in companies’ AI journey as sources of data or key pieces of the AI infrastructure. Moreover there’s a new set projects that are solving PostgreSQL problems with AI.

A boolean value should only contain two values, True or False, but is it correct? Usually people assume so, but sometimes miss the fact that there could be the absence of the value all-together. In databases this is absence is usually stored as NULL and this blog showcases how to find them, use them properly and 11 lessons to learn to be a NULL Pro!

Keep in mind, it’s not only booleans that can contain NULL values, it’s all the columns where you don’t define a NOT NULL constraint!

A boolean value should only contain two values, True or False, but is it correct? Usually people assume so, but sometimes miss the fact that there could be the absence of the value all-together. In databases this is absence is usually stored as NULL and this blog showcases how to find them, use them properly and 11 lessons to learn to be a NULL Pro!

Keep in mind, it’s not only booleans that can contain NULL values, it’s all the columns where you don’t define a NOT NULL constraint!

One of the most common tasks with strings is concatenation! This blog post showcases several techniques to perform it with MySQL.

One of the most common tasks with strings is concatenation! This blog post showcases several techniques to perform it with MySQL.

One of the most common tasks with strings is concatenation! This blog post showcases several techniques to perform it with MySQL.

You have a JSON dataset that you want to upload to a PostgreSQL table containing properly formatted rows and columns… How do you do it?

All the main sources like my own blog and others tell you to load the JSON in a dedicated temporary table containing a unique JSON column, then parse it and load into the target table. However there could be another way, avoiding the temp table!

You have a JSON dataset that you want to upload to a PostgreSQL table containing properly formatted rows and columns… How do you do it?

All the main sources like my own blog and others tell you to load the JSON in a dedicated temporary table containing a unique JSON column, then parse it and load into the target table. However there could be another way, avoiding the temp table!

I was working on some demos recently on the Apache Kafka source connectors (hi #KafkaSummit!), and trying to display the stream of changes in the resulting Apache Kafka topic.

How to load StackOverflow StackExchange data in a PostgreSQL® database!

How to load StackOverflow StackExchange data in a PostgreSQL® database!

How to load StackOverflow StackExchange data in a PostgreSQL® database!

Earlier this month I wrote a piece on solving Gunnar Morling interesting 1 billion rows challenge in PostgreSQL and ClickHouse. Since Aiven provides also MySQL, I wanted to give it a try. TLDR; The results are much slower than PG and ClickHouse, do you have any suggestion on how to improve?

Earlier this month I wrote a piece on solving Gunnar Morling interesting 1 billion rows challenge in PostgreSQL and ClickHouse. Since Aiven provides also MySQL, I wanted to give it a try. TLDR; The results are much slower than PG and ClickHouse, do you have any suggestion on how to improve?

Last week the good old Gunnar Morling launched an interesting challenge about ordering 1 billion rows in Java. Like my ex colleague and friend Robin Moffat, I’m not at all a Java expert, and while Robin used DuckDB to solve the challenge, I did the same with PostgreSQL and ClickHouse.

Need to extract a specific substring out of a text in PostgreSQL®? Read here how!

Last week the good old Gunnar Morling launched an interesting challenge about ordering 1 billion rows in Java. Like my ex colleague and friend Robin Moffat, I’m not at all a Java expert, and while Robin used DuckDB to solve the challenge, I did the same with PostgreSQL and ClickHouse.

When sinking unix timestamps from Apache Kafka to OpenSearch/ElasticSearch using the dedicated connector, they are not recognized by default as timestamp in the target tech.

When sinking unix timestamps from Apache Kafka to OpenSearch/ElasticSearch using the dedicated connector, they are not recognized by default as timestamp in the target tech.

When sinking unix timestamps from Apache Kafka to OpenSearch/ElasticSearch using the dedicated connector, they are not recognized by default as timestamp in the target tech.

When sinking unix timestamps from Apache Kafka to OpenSearch/ElasticSearch using the dedicated connector, they are not recognized by default as timestamp in the target tech.

When sinking unix timestamps from Apache Kafka to OpenSearch/ElasticSearch using the dedicated connector, they are not recognized by default as timestamp in the target tech.

I was working on some demos recently on the Apache Kafka source connectors (hi #KafkaSummit!), and trying to display the stream of changes in the resulting Apache Kafka topic.

You can tabulate a JSON document (retrieve it as a row) in MySQL with the JSON_TABLE function.

You can remove a field from JSON document in MySQL with the JSON_REMOVE function.

PostgreSQL® offers two types of data types to handle JSON data, JSON and JSONB, and provides two different ways to tabulate a JSON to a record:

PostgreSQL® offers two types of data types to handle JSON data, JSON and JSONB, and provides two different ways to tabulate a JSON to a recordset:

PostgreSQL® offers two types of data types to handle JSON data, JSON and JSONB, you can use the - operator to remove an item from an array.

PostgreSQL® offers two types of data types to handle JSON data, JSON and JSONB, and provides two different ways to remove fields from a JSON document:

PostgreSQL® offers two types of data types to handle JSON data, JSON and JSONB, you can use the json_strip_nulls (jsonb_strip_nulls for JSONB) function to remove the null values.

You can insert a field in a JSON document in MySQL with the JSON_INSERT function.

You can edit a JSON document in MySQL with:

• The JSON_SET function that will replace values for JSON paths that exists and add values for the ones that don’t exist.
• The JSON_REPLACE function that will replace values for JSON paths that exists and ignore the ones that don’t exist.

PostgreSQL® offers two types of data types to handle JSON data, JSON and JSONB, you can use the jsonb_set function to remove the null values for JSONB columns.

You can merge two JSON documents in MySQL with:

• the JSON_MERGE_PRESERVE function to concatenate the document values
• the JSON_MERGE_PATCH function to keep the latest value for each key

You can create a JSON document from fields in Mysql® with the JSON_OBJECT function

PostgreSQL® offers two types of data types to handle JSON data, JSON and JSONB, you can use the function json_object (jsonb_object for JSONB) to create a JSON object from an array of key/value pairs.

PostgreSQL® offers two types of data types to handle JSON data, JSON and JSONB, you can use the function json_object (jsonb_object for JSONB) to create a JSON object from two arrays of keys and values.

To get the type of a JSON item in MySQL you need to use the JSON_TYPE function.

To extract an item from an array in MySQL you need to use the -> operator and the [item_number] JSON Path Syntax.

MySQL® offers three ways to extract fields from a JSON object:

• the -> operator to extract the field as JSON
• the ->> operator to extract the field as text
• the JSON_EXTRACT function

Both operators use the JSON Path Syntax

PostgreSQL® offers two types of data types to handle JSON data, JSON and JSONB, and provides several different ways to extract fields from a JSON document:

To get the type of a JSON item in MySQL you need to use the JSON_TYPE function.

PostgreSQL® offers two types of data types to handle JSON data, JSON and JSONB, you can use the function json_typeof (jsonb_typeof for JSONB) to extract the fields type.

To extract an item from an array in MySQL you need to use the -> operator and the [item_number] JSON Path Syntax.

PostgreSQL® offers two types of data types to handle JSON data, JSON and JSONB, you can use the - operator to remove an item from an array.

PostgreSQL® offers two types of data types to handle JSON data, JSON and JSONB, you can use the function json_object (jsonb_object for JSONB) to create a JSON object from an array of key/value pairs.

PostgreSQL® offers two types of data types to handle JSON data, JSON and JSONB, you can use the function json_object (jsonb_object for JSONB) to create a JSON object from two arrays of keys and values.

PostgreSQL® offers two types of data types to handle JSON data, JSON and JSONB, you can use the function array_to_json (array_to_jsonb for JSONB) to create a JSON array from an existing array.

PostgreSQL® offers two types of data types to handle JSON data, JSON and JSONB, you can use the function json_build_array (jsonb_build_array for JSONB) to create a JSON array from a list of elements.

PostgreSQL® offers two types of data types to handle JSON data, JSON and JSONB, and provides several different ways to parse arrays from a JSON document:

MySQL® offers three ways to extract fields from a JSON object:

• the -> operator to extract the field as JSON
• the ->> operator to extract the field as text
• the JSON_EXTRACT function

Both operators use the JSON Path Syntax

To load JSON data into a MySQL column you need to include it as string.

This series covers how to solve common problems on JSON datasets with MySQL and it includes (links will appear once the target pages are up):

So… what does a Staff Developer Advocate do, and how is it different from the Senior Developer Advocate role you were doing before?

I got this question three times in the last two weeks after my promotion from Senior to Staff (🎉🎉🎉🎉), from various people in my network.

So… what does a Staff Developer Advocate do, and how is it different from the Senior Developer Advocate role you were doing before?

I got this question three times in the last two weeks after my promotion from Senior to Staff (🎉🎉🎉🎉), from various people in my network.

PostgreSQL® offers two types of data types to handle JSON data, JSON and JSONB. This doc showcases how to load JSON data into a column.

PostgreSQL® offers two types of data types to handle JSON data, JSON and JSONB. This doc showcases how to index a JSONB column with a GIN index.

PostgreSQL® offers two types of data types to handle JSON data, JSON and JSONB. This doc showcases how to load JSON data into a column.

PostgreSQL® offers two types of data types to handle JSON data, JSON and JSONB, and provides two different ways to tabulate a JSON to a record:

PostgreSQL® offers two types of data types to handle JSON data, JSON and JSONB, and provides two different ways to tabulate a JSON to a recordset:

PostgreSQL® offers two types of data types to handle JSON data, JSON and JSONB, you can use the jsonb_set function to remove the null values for JSONB columns.

PostgreSQL® offers two types of data types to handle JSON data, JSON and JSONB, you can use the - operator to remove an item from an array.

PostgreSQL® offers two types of data types to handle JSON data, JSON and JSONB, and provides two different ways to remove fields from a JSON document:

PostgreSQL® offers two types of data types to handle JSON data, JSON and JSONB, you can use the json_strip_nulls (jsonb_strip_nulls for JSONB) function to remove the null values.

PostgreSQL® offers two types of data types to handle JSON data, JSON and JSONB, you can use the opearator || to concatenate two JSON objects.

PostgreSQL® offers two types of data types to handle JSON data, JSON and JSONB, you can use the function json_object (jsonb_object for JSONB) to create a JSON object from an array of key/value pairs.

PostgreSQL® offers two types of data types to handle JSON data, JSON and JSONB, you can use the function json_object (jsonb_object for JSONB) to create a JSON object from two arrays of keys and values.

PostgreSQL® offers two types of data types to handle JSON data, JSON and JSONB, you can use the function array_to_json (array_to_jsonb for JSONB) to create a JSON array from an existing array.

PostgreSQL® offers two types of data types to handle JSON data, JSON and JSONB, you can use the function json_build_array (jsonb_build_array for JSONB) to create a JSON array from a list of elements.

PostgreSQL® offers two types of data types to handle JSON data, JSON and JSONB, you can use several functions to extract the keys and related values in tabular format.

PostgreSQL® offers two types of data types to handle JSON data, JSON and JSONB, you can use the function to_json (to_jsonb for JSONB) to convert a table row to a JSON object.

PostgreSQL® offers two types of data types to handle JSON data, JSON and JSONB, you can use the function json_pretty (jsonb_pretty for JSONB) to prettify the output.

PostgreSQL® offers two types of data types to handle JSON data, JSON and JSONB, and provides several different ways to parse arrays from a JSON document:

This series covers how to solve common problems on JSON datasets with PostgreSQL® and it includes (links will appear once the target pages are up):

PostgreSQL® offers two types of data types to handle JSON data, JSON and JSONB, and provides several different ways to extract fields from a JSON document:

PostgreSQL® offers two types of data types to handle JSON data, JSON and JSONB, you can use the function json_typeof (jsonb_typeof for JSONB) to extract the fields type.

PostgreSQL® offers two types of data types to handle JSON data, JSON and JSONB. This doc showcases how to index a JSONB column with a GIN index.

PostgreSQL® offers two types of data types to handle JSON data, JSON and JSONB, and provides several different ways to check if a value/field from a JSON document:

PostgreSQL® offers two types of data types to handle JSON data:

• JSON stores the JSON as text, performing a validation on the correctness of the JSON syntax
• JSONB optimizes the JSON storage in a custom binary format. Therefore, on top of validating the correctness of the JSON format, time is spent to properly parse and store the content.

PostgreSQL® offers two types of data types to handle JSON data, JSON and JSONB. This doc showcases how to load JSON data into a column.

PostgreSQL® offers two types of data types to handle JSON data, JSON and JSONB. This doc showcases how to index a JSONB column with a GIN index.

Sometimes you want to query a remote InfluxDB server to understand the data in it. This can be done via cURL as explained in the InfluxDB docs

This page contains all the links related to the SOFT (Scalable Observable Fast Trustworthy) methodology for defining robust data platforms.

Technical scalability is one of the main drivers of a data platform, as mentioned in the SOFT methodology. But what are the options? In this blog we’ll evaluate the direction of scalability and the tradeoffs you might encounter in the decision process.

Companies are in a continuous motion: new requirements, new data streams, new technologies are popping up every day. When designing new data platforms supporting the needs of your company, failing to perform a complete assessment of the options available can have disastrous effects on a company’s capability to innovate, and making sure their data assets usable and reusable in the long term.

This page contains all the links related to the SOFT (Scalable Observable Fast Trustworthy) methodology for defining robust data platforms.

Companies are in a continuous motion: new requirements, new data streams, new technologies are popping up every day. When designing new data platforms supporting the needs of your company, failing to perform a complete assessment of the options available can have disastrous effects on a company’s capability to innovate, and making sure their data assets usable and reusable in the long term.

This page contains all the links related to the SOFT (Scalable Observable Fast Trustworthy) methodology for defining robust data platforms.

Technical scalability is one of the main drivers of a data platform, as mentioned in the SOFT methodology. But what are the options? In this blog we’ll evaluate the direction of scalability and the tradeoffs you might encounter in the decision process.

PostgreSQL® offers two types of data types to handle JSON data, JSON and JSONB, and provides two different ways to tabulate a JSON to a record:

PostgreSQL® offers two types of data types to handle JSON data, JSON and JSONB, and provides two different ways to tabulate a JSON to a recordset:

PostgreSQL® offers two types of data types to handle JSON data, JSON and JSONB, and provides two different ways to remove fields from a JSON document:

PostgreSQL® offers two types of data types to handle JSON data, JSON and JSONB, you can use the - operator to remove an item from an array.

PostgreSQL® offers two types of data types to handle JSON data, JSON and JSONB, you can use the json_strip_nulls (jsonb_strip_nulls for JSONB) function to remove the null values.

PostgreSQL® offers two types of data types to handle JSON data, JSON and JSONB, you can use the opearator || to concatenate two JSON objects.

PostgreSQL® offers two types of data types to handle JSON data, JSON and JSONB, you can use the function json_object (jsonb_object for JSONB) to create a JSON object from an array of key/value pairs.

PostgreSQL® offers two types of data types to handle JSON data, JSON and JSONB, you can use the function json_object (jsonb_object for JSONB) to create a JSON object from two arrays of keys and values.

PostgreSQL® offers two types of data types to handle JSON data, JSON and JSONB, you can use the function json_object (jsonb_object for JSONB) to create a JSON object from an array of key/value pairs.

PostgreSQL® offers two types of data types to handle JSON data, JSON and JSONB, you can use the function json_object (jsonb_object for JSONB) to create a JSON object from two arrays of keys and values.

PostgreSQL® offers two types of data types to handle JSON data, JSON and JSONB, you can use the function array_to_json (array_to_jsonb for JSONB) to create a JSON array from an existing array.

PostgreSQL® offers two types of data types to handle JSON data, JSON and JSONB, you can use the function to_json (to_jsonb for JSONB) to convert a table row to a JSON object.

PostgreSQL® offers two types of data types to handle JSON data, JSON and JSONB, you can use the function json_build_array (jsonb_build_array for JSONB) to create a JSON array from a list of elements.

PostgreSQL® offers two types of data types to handle JSON data, JSON and JSONB, you can use several functions to extract the keys and related values in tabular format.

PostgreSQL® offers two types of data types to handle JSON data, JSON and JSONB, you can use the function to_json (to_jsonb for JSONB) to convert a table row to a JSON object.

PostgreSQL® offers two types of data types to handle JSON data, JSON and JSONB, you can use the function json_pretty (jsonb_pretty for JSONB) to prettify the output.

PostgreSQL® offers two types of data types to handle JSON data, JSON and JSONB, and provides several different ways to parse arrays from a JSON document:

Companies are in a continuous motion: new requirements, new data streams, new technologies are popping up every day. When designing new data platforms supporting the needs of your company, failing to perform a complete assessment of the options available can have disastrous effects on a company’s capability to innovate, and making sure their data assets usable and reusable in the long term.

I started following the #PGSQLPhriday initiative a couple months back but never had the time to properly sit down and write due to conference traveling. Therefore I was super happy to be at home this week and find out that PGSQL Phriday #003 theme is about the PostgreSQL community!

If you’re reading this, there are good chances you’re thinking, writing or iterating over an abstract for a conference. The post is about little tips I learnt in more than 8 years of prepping for conferences and in 1 year or reviewing internal abstracts at Aiven before they are submitted for a conference.

If you’re reading this, there are good chances you’re thinking, writing or iterating over an abstract for a conference. The post is about little tips I learnt in more than 8 years of prepping for conferences and in 1 year or reviewing internal abstracts at Aiven before they are submitted for a conference.

If you’re reading this, there are good chances you’re thinking, writing or iterating over an abstract for a conference. The post is about little tips I learnt in more than 8 years of prepping for conferences and in 1 year or reviewing internal abstracts at Aiven before they are submitted for a conference.

Sometimes you want to query a remote InfluxDB server to understand the data in it. This can be done via cURL as explained in the InfluxDB docs

Sometimes when defining an Apache Flink® table using SQL we need to map an epoch timestamp and use it as record/message timestamp, this blog contains few tricks to get it right.

Sometimes when defining an Apache Flink® table using SQL we need to map an epoch timestamp and use it as record/message timestamp, this blog contains few tricks to get it right.

How to define a database connection in a JSON file that can be imported in PG Admin 4

How to define a database connection in a JSON file that can be imported in PG Admin 4

How to define a database connection in a JSON file that can be imported in PG Admin 4

Now and then I get asked what to do and where to eat nearby Verona, so there, you have the list. I’ll update the list whenever needed

Now and then I get asked what to do and where to eat nearby Verona, so there, you have the list. I’ll update the list whenever needed

Now and then I get asked what to do and where to eat nearby Verona, so there, you have the list. I’ll update the list whenever needed

Every time someone talks about slides design a great debate is generated. People have opinions and, guess what? Specifically in the area of public speaking, they seem to be quite vocal about them 🤣.

Every time someone talks about slides design a great debate is generated. People have opinions and, guess what? Specifically in the area of public speaking, they seem to be quite vocal about them 🤣.

Every time someone talks about slides design a great debate is generated. People have opinions and, guess what? Specifically in the area of public speaking, they seem to be quite vocal about them 🤣.

I was working on some demos recently on the Apache Kafka source connectors (hi #KafkaSummit!), and trying to display the stream of changes in the resulting Apache Kafka topic.

A month has already gone since my start at Aiven.io and it’s time for a first, and brief, look back at my experience so far together with few suggestions (even if nobody asks) for anyone that will follow my steps.

TL;DR: The journey has been great, met several amazing people and learnt lots using cool tech! A bit shocked about food choices… but that’s life 🤌 !