A data model should show the relationships that exist between various customers, concepts, products, among many. Data Modeling describes the creation of a visual representation (a chart or diagram) of a data system, or parts of that system. It is used to display and communicate connections between objects/concepts and shows their relationships. The goal of a data model is to show what kind of data is being used and stored, the relationships between these entities (objects that exist), and ways the data can be organized.
The more technologies being used, the more complex its data model. The different departments of an organization may be visualized as the links of a chain, with some links (departments) using different technologies. As a result, not all the links in this chain are altered with a single command, and not at the same time. According to Pascal Desmarets, Founder and CEO of Hackolade:
“The ability to handle complexity and scale is another challenge. It seems that companies are having hundreds, sometimes thousands, if not tens of thousands, of APIs and microservices, and these are handled using dozens of different technologies. Their schemas are flying around, each of them having its own lifecycle.”
With the creation and collection of data continuously growing, extracting insights from the overabundance of data available needs well-designed data models. Developing a good data model requires an understanding of the organization’s processes, customers, and services/products, as well as the ability to organize those processes, etc., in a visual format. Data models should be designed and built around the organization’s goals and needs.
Some of the significant Data Modeling trends for 2022 include new toolkits, modeling of data lakes, and an expansion of non-relational modeling techniques.
Time-series Data Modeling
A time-series database is built for the specific purpose of storing records that are earmarked with timestamps. It is designed to keep track of both time and changes, and of changes to the information that happen over time. Modeling time-series databases is expected to continue expanding through 2022.
Toolkits for JSON Data Modeling
JavaScript Object Notation (also known as JSON) has become a standard for internet communications. Both data platforms and NoSQL databases have embraced it as a standard, and it should continue to grow in popularity.
Data Lake Models
Data warehouses have increasingly had problems keeping up with the rising amount of gathered and stored data. The need for centralized repositories accepting both structured and unstructured data has become conspicuous. Data lakes provide a solution.
Data lakes will accept raw unstructured data, as well as structured data, from any source. Once the data is collected, it can be transformed into structured data that can be used for data analytics, SQL manipulation, machine learning, and more.
Industry-specific Modeling
Digital transformation is impacting nearly every industry, resulting in novel data model applications unique to that industry. It is not uncommon for vendors to offer industry-specific data models with all the needed data structure designs and governance requirements. It allows businesses to adopt models specific to their needs.
Data Modeling Tools
A good data model clearly identifies customers, products, etc., and the relationships between them. Finding and listing these entities (objects that exist) and their relationships with other entities can be time-consuming and tedious. Data Modeling tools can make this laborious job much, much easier. There are a variety of data modeling tools, with some supporting Windows, and others supporting Mac and Linux. There are a variety of factors to consider when choosing a Data Modeling tool.
These are just a few of the tools on the market that can help organizations with their Data Modeling needs.
Idera’s ER/Studio: A popular Data Modeling tool that lists data assets and sources from across different database platforms, and then builds the data models needed. It is compatible with Mac, Linux, and Windows. Businesses can model and understand relationships between people, processes, and data.
Erwin Data Modeler: This is also one of the more popular Data Modeling tools available. It is used to locate, visualize, design, standardize, and deploy high-quality data assets.
Navicat Data Modeler: A powerful Data Modeling tool that is cost-effective and supports the creation of high-quality data models for a variety of use cases. It allows users to visually design and print data models, import models from OBDC (Open Database Connectivity) sources, and perform both forward and reverse engineering. This tool can be used with a number database systems.
Lucidchart: A cross-platform collaboration tool that combines user-friendliness with strong functionality. It helps to create process maps, organizational charts, concept maps, and more. It is a cloud-based Data Modeling tool, and can save significant amounts of manual labor. It works well with various platforms (MySQL, SQL Server, Oracle, and PostgreSQL).
Toad Data Modeler: This tool has powerful query tuning capabilities (making SQL queries as fast as possible). Its primary strength is its ability to compare and sync data from different servers. It performs automated tasks, saving time and increasing efficiency.
ConceptDraw Diagram: This is an easy-to-use Data Modeling tool that can create infographics, business graphics, diagrams, and flowcharts. It is described as an intuitive database tool that saves lots of time. It comes with an Entity Relationship Diagram modeling component that will help to create a complete relational database.
Data Modeling and the Cloud
As businesses migrate to the cloud, use of Data Modeling can help management to make intelligent decisions. Understanding how to adapt technologies such as business intelligence models, ETL, and data streaming begin with a focus on modeling the underlying data. Data models have become crucial for working with the cloud.
The architecture of clouds typically separate their computing services from their storage services (computing services are often charged on an hourly basis, while storage service charges are based on volume and usage). Additionally, the cloud’s data warehouse does not index the stored data, as a private, in-house data warehouse would. This makes establishing relationships more difficult when using data modeling with the cloud. As a consequence, data preparation tools have become quite popular, and should be included in the data model.
A few of the many data preparation tools to assist with the cloud and Data Modeling on the market include:
Altair Monarch: A desktop self-service data preparation tool capable of connecting varying data sources including big data, unstructured data, and structured data. It comes with over 80 data preparation functions.
Alteryx Designer: Features a user-friendly interface that cleanses data from cloud applications, data warehouses, spreadsheets, and other sources. It also improves Data Quality, data transformation, and data integration. Alteryx Designer will blend data for spatial data files, allowing them to be joined with third-party data.
Anzo: With this tool, users can find, connect, and blend their data. Anzo will connect with external and internal data sources, including private, on-premise data lakes or the cloud. It also supports data cataloging that uses graph models.
Datameer Enterprise: This tool offers a data analytics lifecycle that supports data preparation, exploration, and use. The spreadsheet-style interface allows complex data to be blended for the development of data pipelines.
Infogix Data360 Analyze: This tool is flexible and easy to use. It provides a suite of integrated governance features that includes metadata management, data cataloging, data lineage, and business glossaries. It also provides customizable dashboards that adapt with organizational data changes. Infogix is useful for Data Governance and compliance.
Paxata Self-Service Data Preparation: This is an application within the “Adaptive Information Platform.” It is built on a visual use interface and uses spreadsheet metaphors.
A few years ago, there were many people saying that Data Modeling was now going to be dead, especially due to the rise of machine learning, AI, self-service tools, and non-relational databases. But the opposite is now the case. Data Modeling is more important than ever. Without good models then data scientists, business analysts, and database engineers don’t know where their data is coming from and consistently run into Data Quality problems. Long live Data Modeling!
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