Tag: data

Why we need to Hash our IDs in Data Vault 2.0

December 24, 2022 / albertgd / 0 Comments

Hashing is a technique used to transform a piece of data, such as an identifier, into a fixed-size string of characters, known as a hash. The resulting hash is unique to the input data and has a fixed length, making it useful for various purposes, including data storage and retrieval.

In the context of data vault 2.0, hashing is used to obscure the original values of identifiers in order to protect the privacy of individuals or organizations. By replacing sensitive identifiers with hashed values, it becomes more difficult for unauthorized parties to access or use the original data. This can be especially important when dealing with sensitive or personal information, such as social security numbers or financial data.

Hashing will make an ID totally impossible to get any meaning unless we have the connection between the original ID and the hashed ID

Hashing can also be useful for improving the performance of data management systems by allowing for faster searches and comparisons. When data is stored in a hashed format, it can be more efficiently indexed and retrieved, which can help to reduce the time and resources required to access and process the data.

Overall, hashing is an important technique in data management and can be used to protect the privacy and security of data, as well as improve the performance of data management systems.

Advantages of Hashing IDs in DV2.0

There are several advantages to hashing identifiers in data vault 2.0:

Protects privacy: Hashing can help to protect the privacy of individuals or organisations by obscuring sensitive identifiers such as social security numbers or financial data. This can be especially important when dealing with sensitive or personal information.
Improves security: By replacing sensitive identifiers with hashed values, it becomes more difficult for unauthorized parties to access or use the original data. This can help to improve the overall security of the data vault.
Improves performance: Hashing can help to improve the performance of data management systems by allowing for faster searches and comparisons. When data is stored in a hashed format, it can be more efficiently indexed and retrieved, which can help to reduce the time and resources required to access and process the data.
Reduces storage requirements: Hashing can help to reduce the amount of storage space required to store data, as the resulting hash is typically smaller than the original identifier. This can be especially useful when dealing with large datasets.

Overall, hashing is a useful technique in data management and can provide a number of benefits in the context of data vault 2.0.

Best practices when splitting your satellites in Data Vault modelling

December 23, 2022 / albertgd / 0 Comments

In data vault modeling, satellites are used to store historical and contextual data about an entity in a data vault. When splitting satellites, it is important to consider the following best practices:

Use a consistent and logical naming convention: Use a naming convention that is easy to understand and follow. This will help you easily identify and locate the satellites you need.
Keep related data together: Group data that is related or belongs to the same entity in the same satellite. This will make it easier to understand and analyse the data.
Avoid overloading satellites: Avoid adding too much data to a single satellite. If a satellite becomes too large, it can be difficult to manage and maintain.
Use the correct data types: Make sure to use the correct data types for each attribute in the satellite. This will ensure that the data is stored and used efficiently.
Consider data integrity: When splitting satellites, make sure to consider the impact on data integrity. You want to ensure that you do not lose any data or create inconsistencies when splitting the satellites.

By following these best practices, you can ensure that your satellites are organised and maintained in a way that makes it easy to understand and use the data in your data vault.

Criteria to follow when splitting your Satellites

There are a few steps you can follow to split your satellites in data vault modeling:

Identify the reason for splitting: Determine the reason for splitting the satellites. This could be because the satellite has become too large, or because you want to group related data together in a more logical way.
Determine the criteria for splitting: Decide on the criteria for splitting the satellite. This could be based on the type of data being stored, the time period it covers, or any other relevant factors.
Create a new satellite: Create a new satellite for the data that meets the splitting criteria. Make sure to use a consistent and logical naming convention, and to include all relevant attributes in the new satellite.
Migrate the data: Migrate the data from the old satellite to the new satellite. Make sure to carefully check the data to ensure that it has been migrated correctly and that there are no inconsistencies or data loss.
Update any related links: If the satellite being split is linked to other objects in the data vault, make sure to update the links to point to the new satellite.

Avoiding when splitting your Satellites

When splitting satellites in data vault modeling, it is important to avoid the following:

Losing data: Make sure to carefully migrate all data from the old satellite to the new satellite, to ensure that no data is lost during the split.
Creating inconsistencies: Pay attention to the data being migrated, to ensure that it is consistent and that there are no inconsistencies introduced during the split.
Overloading the new satellite: Avoid adding too much data to the new satellite. If a satellite becomes too large, it can be difficult to manage and maintain.
Using an inconsistent naming convention: Make sure to use a consistent and logical naming convention when creating the new satellite. This will help you easily identify and locate the satellite in the future.

By avoiding these pitfalls, you can ensure that the process of splitting your satellites in the data vault is smooth and does not compromise the integrity of your data

Using Variant data type on DV2.0

December 22, 2022 / albertgd / 0 Comments

What is Variant data type?

A variant data type is a data type that can store a range of different data types, including numerical, string, and Boolean values. This allows for greater flexibility in storing data, as the same field can be used to store a variety of different types of data.

In some programming languages, the variant data type is called “dynamic” or “any” data type. It is often used in situations where the type of data being stored is not known in advance, or where the data may change over time.

For example, in a Data Vault modelling scenario, a variant data type could be used to store data from a variety of sources, including databases, flat files, and APIs. This allows the Data Vault to accommodate a wide range of data types and formats, without requiring any transformation or cleansing of the data.

Variant data types on Data Vault 2.0

Using variant data types in Data Vault modelling can provide a number of advantages. Some of these advantages include:

Improved data integrity: By using Variant data types, you can ensure that the data stored in your Data Vault is accurate and consistent, even when the data sources may be inconsistent or incomplete. This helps to ensure the quality and reliability of your data.
Enhanced flexibility: Variant data types allow you to store data flexibly, which can be helpful when dealing with data that may change over time or may be incomplete. This can help to reduce the need for data cleansing or transformation, as the Data Vault can accommodate a wide range of data types and formats.
Improved performance: Using variant data types can help improve your Data Vault’s performance, as the system can more efficiently store and retrieve data. This can be particularly important when working with large volumes of data.
Improved scalability: By using Variant data types, you can more easily scale your Data Vault to accommodate growing volumes of data. This can help to ensure that your Data Vault remains functional and efficient as your data needs change over time.

Overall, using Variant data types in Data Vault modelling can help to improve the accuracy, flexibility, performance, and scalability of your data management system.

Where can I find Variant data types?

Variant data types are supported by a wide range of systems and programming languages. Some examples include:

Databases

Microsoft SQL Server: In SQL Server, the “sql_variant” data type is used to store values of different data types. This data type can store values of any data type, except for “text,” “ntext,” and “image” data types.
MySQL: In MySQL, the “JSON” data type is used to store values in a variant data format. The JSON data type can store values of any data type, including arrays and objects.
PostgreSQL: In PostgreSQL, the “anyarray” data type is used to store arrays of values of any data type. The “anyelement” data type can be used to store values of any data type, including arrays and objects.
Oracle Database: In Oracle Database, the “ANYDATA” data type is used to store values of any data type. This data type can be used to store values of any type, including integers, strings, and objects.

Cloud database providers

Amazon Web Services (AWS): AWS offers a number of cloud databases that support variant data types, including Amazon Aurora, Amazon DynamoDB, and Amazon Redshift.
Microsoft Azure: Microsoft Azure offers a number of cloud databases that support variant data types, including Azure SQL Database, Azure Cosmos DB, and Azure Synapse Analytics (formerly SQL Data Warehouse).
Google Cloud Platform (GCP): GCP offers a number of cloud databases that support variant data types, including Cloud Bigtable, Cloud Datastore, and Cloud SQL.
Snowflake: In Snowflake, the “VARIANT” data type is used to store values of different data types in a single field. The VARIANT data type can store values of any data type, including integers, strings, and objects.

In addition to the VARIANT data type, Snowflake also supports a number of other data types, including numeric, string, Boolean, and date/time data types. These data types can be used to store specific types of data in a more efficient manner, depending on the needs of the application or query.

Programming languages

Python: In Python, the “Any” data type is used to store values of any data type. This data type is part of the “typing” module and can be used to store values of any type, including integers, strings, and objects.
Java: In Java, the “Object” data type is used to store values of any data type. This data type is part of the Java language and can be used to store values of any type, including integers, strings, and objects.

Variant will help you to develop in a more agile way

Using variant data types can help to create a more agile data warehouse development process by providing greater flexibility in storing and managing data. Some specific ways in which variant data types can improve the agility of data warehouse development include:

Accommodating changing data sources: By using Variant data types, you can more easily accommodate changes to the data sources that feed your data warehouse. For example, if a new data source becomes available or an existing data source changes its schema, you can use variant data types to more easily incorporate the new data into your data warehouse.
Simplifying data cleansing and transformation: By using Variant data types, you can often reduce the need for data cleansing and transformation, as the data warehouse can more easily accommodate a wide range of data types and formats. This can help to streamline the data load process and reduce the time and effort required to get new data into the data warehouse.
Improving query performance: By using Variant data types, you can often improve the performance of queries against your data warehouse, as the system can more efficiently store and retrieve data. This can be particularly important when working with large volumes of data.