Database Design Consideration – Role of Schemas
If you are a decision-maker at a company that handles vulnerable data within a legacy system, you may be looking for information about cloud migration but worry about compromising security and data. Before you land on an approach and strategy for your company, you should understand the information topology. This is where types of database schemas, schema implementation requirements, schema mappings as technical and business rules applied during data migration and accelerators in form of technology come handy.
At RightData, we serve midsized to large organizations with hundreds of employees and a massive backlog of data. We offer Dextrus, our high-performance cloud data platform which can be an effective accelerator (serving all data practitioner initiatives) that can cut-short your time to realization efficiently.
Understanding Database Schema
Schema is another word for how your data organization strategy is implemented. Your needs will adapt as your company grows and implements new features into your application database. However, poorly managed schema (as part of change management) can lead to significant downtimes and data quality problems. Your schema requires consistent monitoring for changes to your data, and routine audits can reveal issues in your data ecosystem and ensure data schema like columns, tables and data types, relationships are timely updated and accurate.
What Is Data Schema?
Your database schema defines data organization in a database, including logical constraints like data types, table names, fields and relationships between these entities. Typically, a schema uses visual representations to communicate the database's architecture. One way to manage schema is thru Data modeling. Data Models can help a database architects and administrators to design optimal data solutions that help avoid duplication and process redundancies. These also help support the needs from data democratization, wherein the entire enterprise benefits from clear and well defined standards
Database Schema vs. Database Instance
Though related and sometimes mistakenly conflated, a few notable differences exist between a database schema and a database instance.
- Database schema: You can think of a database schema like a blueprint. A database schema details how the data relates to other data models or tables. However, it does not include data. A database schema is relatively static, meaning it doesn't change frequently.
- Database instance: On the other hand, a database instance is a snapshot of data pulled from a database during one specific moment. A database instance includes actual information in form of table records. Unlike a database schema, a database instance is likely to change or vary over time.
Database schemas and instances influence each other via the database management system. This system ensures every database instance complies with the constraints the designers impose on the schema.
Types of Database Schemas
There are a few different types of database schemas, including a conceptual, logical and physical.
1. Conceptual Database Schema
A conceptual database schema provides a comprehensive view of what your system will include, the business rules involved and the overall organization methods used. Typically, conceptual database schemas are integral to collecting initial project requirements. This schema type usually details how the user interacts with a database system.
2. Logical Database Schema
A logical database schema details the constraints that should apply to stored data. A logical database schema is less abstract than other schemas. Schema objects have defining information, such as:
- Field names
- Table names
- Integrity constraints
- Entity relationships
Integrity constraints typically do not have technical requirements. Logical schemas are close to business terminology, the names of the entities and attributes are un-abbreviated. You can develop logical database schemas and demonstrate connections between components of your organization's data thru a process known as entity-relationship modeling.
3. Physical Database Schema
The logical schema goes into representing a physical database schemas. This is when the detail concrete storage options like volumetric (record counts), data types, lengths of data types, indexes and other physical organization of data like partitions are defined. A physical database scheme typically deployed as a Data Definition language (DDL) creates data base objects at the disk level.
Schema Implementation Requirements
Integrating several sources in the same schema can be beneficial. Meet the following requirements to make schema implementation a smooth process.
- Minimality: Ideally, the migration should preserve elements from all sources.
- Normalization: In the database schema, independent relationships and entities shouldn't be in the same table. Proper normalization helps avoid redundancies with data storage.
- Extended overlap preservation: Any information that only appears in a single source should transfer to the database schema, as long as it associates with the overlapping elements.
- Overlap preservation: The database schema table should contain each overlapping element in the integrated schemas.
To learn more about schema implementation requirements, reach out to us at RightData. We can provide you with further information that will be relevant and useful to your organization.
Using Schema in Data Migration
Data migration is the first part of re-platforming a database in a data engineering initiative. Embedded Dextrus connectors can migrate data from a legacy platform to a modern cloud database. Cloud databases may include various Azure databases, Google Big Query and AWS Redshift.
Dextrus can migrate data at different levels, such as the schema level, database level or an individual table level, using simple configuration steps. With Dextrus, your organization doesn't need to execute DDL scripts for developing target table schemas before the data migration. Whether you are upgrading the version of your database, transitioning your on-premise system to cloud database migration or migrating your data due to a merger, there are a few signs of a successful data migration to look for:
- Profile your data
- Gain insight on your data
- Connect to each data point
- Reconcile and validate data between target databases and sources once the migration completes
An essential part of data migration is the method used.
Phased Data Migration Approach
The phased data migration approach divides the process of data migration into multiple sub-processes based on functional areas. The significant advantage to this method is that your old system remains accessible and online during the data migration.
This approach tends to be more complex, but it is often beneficial for organizations that have large volumes and cannot afford any downtime. Your legacy and modern data platforms will still run, and quality control processes ensure a successful data migration without shutting down specific functional areas in your system.
Big Bang Data Migration Approach
The big bang data migration approach is faster, economical and less complex. Your organization may prefer this method depending on how much data you will be migrating. It only takes one step to transfer data from your legacy system to a modern platform. However, this method takes your system offline until the migration finishes.
If your organization has a smaller volume of data, you may want to choose this approach. However, if your organization cannot afford any downtime, this may not be a wise option.
Key Benefits of Database Schemas
Database schemas and objects are essential to ensuring efficiency in your organization's everyday operations as big data continues growing. A poorly documented, disorganized relational model will be harder to maintain, which could pose issues for your company and users. Database schemas can offer the following benefits:
- Streamline organization and communication: A database schema's documentation enables better communication between internal stakeholders and greater organization. Additionally, documentation allows users to understand aggregation methods for each table and the logical constraints.
- Easy access and security: Database schema design allows people to organize information into separate entities, which makes information sharing easier. Additionally, administrators can control access via database permissions to add an extra security layer to your proprietary data. For example, if a schema includes personally identifiable information, you should encrypt it for privacy.
- Ensure data integrity: Your database schema can also help validate your organization's data. Administrators can avoid data duplication by managing normalization processes. Database schemas can help you monitor compliance and stick to properties of atomicity, consistency, isolation and durability.
Other benefits of using schemas include -
- Easily access and manipulate objects
- Manage database objects in a database thru organization within a schema
- Apply security permission to protect and separate database objects depending on user access rights
If you want to enjoy these advantages and learn more about database schema, turn to RightData. We can answer your questions and address any concerns you may have.
Seamless Database Migration Solutions From RightData
For seamless database migration solutions, try our Dextrus solution. This off-the-shelf data integration tool has all the capabilities your organization will need. Before the migration, use Dextrus to improve data quality. Data engineers can take this opportunity to address any issues to ensure the new platform includes accurate information. Leading features include the following.
- Data validation: Use the De-Dups transformation node and the aggregation transformation node to achieve data validation and configure pipelines for data transfer.
- Anomaly detection: Dextrus' data-wrangling component caps anomalies based on built-in rules.
- Embedded analytics: The Dextrus platform has embedded analytics to help you visualize the data. Use the Analytics node to build the pipeline. Create visualizations in the pipeline or the source and target databases for speedy data insights. The embedded analytics component is beneficial for making decisions regarding data.
- Insight into datasets: Get quick insights on your data with the Spark SQL engine.
- Easy data preparation: The tool palette's transformation nodes are useful for analyzing the data's grain, nulls and empty records and distribution of the attributes.
- Push-down optimization: Dextrus helps your organization achieve optimization via push-down-enabled transformation nodes, pushing down transformation logic to the source or target database.
Dextrus can assist your organization with data transformations, ingestion, cleansing, streaming, preparation, reporting, wrangling and machine learning modeling. In minutes, you can develop real-time and batch streaming data pipelines. You can also use Dextrus for data analytics and reporting and maintaining an accessible cloud data lake. Gain and analyze insights with dashboards and visualizations. Enjoy the following benefits when you choose Dextrus for your organization:
- High throughput
- Low data latency
- Small learning curve
- Cloud data platform configuration
- Data-driven pipeline configuration
Book a Dextrus Demo From RightData
At RightData, we offer two platforms to simplify your company's complex data operations processes, which means we can give data practitioners the tools needed to gain more insights into data. Our vision as a data software solutions company is to empower your organization with a solution that accelerates data innovation. In a unified collaborative platform, we democratize data quality, data science and data engineering.
When you can accelerate value creation from your data, you can determine the best decisions at the lowest cost and in the quickest time to deliver the most value. Our Dextrus platform allows your organization to transform your raw data into valuable insights. Contact us at RightData to learn more or book a Dextrus demo today.