Your business is constantly dealing with streams of data. With so much data needing processing, collecting, and organizing, modern companies need a way to manage it effectively.
Enter modern data platforms (MDPs). These platforms are reliable solutions for managing and leveraging all your data. MDPs make optimizing your operation easier than ever. Understanding data platform capabilities can help you unlock your data’s full potential.
What Is a Data Platform?
A data platform is a central space that holds and processes your data. A unified data platform takes all your data from each source and collects, manages, stores, and analyzes it. Traditionally, data platforms had limited data-handling abilities. They often had data silos — data stores that were disconnected from the rest of the data. Modern data platforms, however, are more advanced and convenient.
An MDP is a data platform designed to handle the data demands of the modern day. These data platforms are built to handle data from multiple sources. They can easily scale with your needs, processing data in real time and giving you the tools to analyze it effectively. Big data platforms are a version of MDPs that work with data on a vast scale. With a quality MDP, you can make more accurate decisions, adapt quickly to market changes, and maintain productivity.
Modern Data Platform Features
An MDP is a more advanced enterprise data platform (EDP) version. EDPs manage all your data in a central hub. At the same time, MDPs take this feature and add to it with data analysis, decision-making, and even machine learning (ML) or artificial intelligence (AI). You can break MDPs down into several key components that work together to maximize your data use:
Data ingestion: This is the first step. Your MDP collects and imports data from databases, sensors, application programming interfaces, and more. Data flows into and through the MDP, collecting in a central space.
Data storage: Once ingested, the MDP stores your data. Data warehouses and cloud-based data storage spaces can hold significant amounts of data. Storage is set up for easy organization and retrieval.
Data processing: After ingestion and storage, data needs processing. Processing takes the data and turns it into an analyzable format. Data processing includes batch and real-time processing, allowing you to instantly receive information on your data.
Analytics: Next comes analytics. MDPs take your data and use various tools to find patterns and insights. These analytics give you an unmatched understanding of your data, letting you make more strategic decisions.
Security and compliance: MDPs come with strong security measures to prevent data from becoming vulnerable to attacks and other incidents. Security is essential for protecting data and maintaining data regulation compliance.
Orchestration: Orchestration involves getting everything where it needs to be when it needs to be there. It oversees two processes — moving data between components and automating workflows.
Modern Data Platform Applications Across Industries
Modern data platforms allow industries to manage their data more effectively. With the right MDP, your company can easily manage data and derive better insights. Here are some data platform examples in different industries:
Manufacturing: Predictive maintenance data lets manufacturing companies know when to send equipment for upkeep. Additionally, MDPs can improve quality control efforts by checking data.
Retail: The retail industry uses MDPs to analyze customer behavior and personalize shopping experiences.
Health care: MDPs in health care settings streamline operations and improve the patient experience. Health data needs secure protection and efficient management to meet compliance and improve care standards.
Financial: The financial sector relies on MDPs to detect fraud, personalize products, and assist with risk management.
Benefits of Modern Data Platforms
If you’re looking to overhaul your business’s approach to data, MDPs can help. Consolidating data and improving its management has many benefits for your operation, including:
Improved decision-making: Better data processing and real-time analytics boost your decision-making capabilities. Teams can use accurate, up-to-date data to respond quickly and effectively to market changes, customer needs, and other challenges.
Enhanced performance: MDPs are designed to handle massive amounts of data while adjusting to your needs. MDPs scale with your data, efficiently managing everything without slowing down.
Cost-efficiency: Traditional manual data handling is expensive to scale and maintain. MDPs let you only pay for what you use, ensuring you work within your budget and needs.
Future-proofing: As technology changes and data needs grow, MDPs can evolve with them. Incorporate new tools, data sources, and technology into your MDP without overhauling your central infrastructure.
Potential Challenges in Implementing Data Platforms
While data platforms are excellent tools for handling data, getting the infrastructure in place can be challenging. Investing in the right partner is essential for ensuring you have the support you need for success. Some data platform challenges you might face are:
Integration complexities: Integrating your diverse data sources and systems can be challenging. Legacy systems often struggle to work with modern platforms. It takes a quality platform and expert support to make your data flow seamless.
Data quality and consistency: Data quality is key for strategic decision-making. However, integrating data from different sources can lead to duplicates, errors, and incomplete data. To ensure accurate data, you need processes for cleaning, standardizing, and validating data.
Security concerns: More centralized data can also mean more cyberattack threats. You need an MDP with strong security measures to protect your data from cyberattack threats.
Skill gaps and resource allocation: MDPs can require specialized skill sets in data analytics and engineering. Finding the talent to manage your MDPs can strain your current budget and resources.
The Future of Modern Data Platforms
As advanced as current MDPs are, they’re only going to become more powerful. AI and ML are changing how we approach data. Automating data processing allows these strategies to deliver faster, more accurate insights.
AI-driven platforms can spot patterns, predict trends, and make decisions independently. Using AI can also free up your human talent for more complex tasks. ML models improve with every piece of data they learn from. They can develop advanced predictive capabilities the longer you use them.
JumpStart Your Data Platform Journey
Your data is one of your most valuable assets. Fully harness your data and drive innovation with help from Kopius. We specialize in helping businesses leverage advanced data analytics, machine learning, data governance, and more to make smarter, data-driven decisions.
Whatever your challenges, our experts are here to help. We provide comprehensive data solutions tailored to your unique needs. With Kopius, you can create insightful dashboards, improve data security, and more.
From retail to aerospace industries, managing your data effectively and securely is critical to your overall business objectives. Data storage comes in many shapes and sizes, especially with the advancements in modern digital technology. To properly store large amounts of data, you need the right location. While a database on a computer might be enough to make data accessible for a small business, a large enterprise likely requires a data warehouse or data lake.
How do you find the ideal solution? The first step is to consider the type of data you need to store and how you will use it. No data strategy is the same, so it’s important to understand how data solutions can be tailored to meet your needs.
What Is a Database?
A database is a type of electronic storage location for data. Businesses use databases to access, manage, update, and secure information. Most commonly, these records or files hold financial, product, transaction, or customer information. Databases can also contain videos, images, numbers, and words.
The term “database” can sometimes refer to “database management system” (DBMS), which enables users to modify, organize, and retrieve their data easily. However, a DBMS can also be another application or the database system itself.
There are many different types of databases. For example, you may consider a smartphone a database because it collects and organizes information, photos, and files. Businesses can use databases on an organizational-wide level to make informed business decisions that help them grow revenue and improve customer service.
Some key characteristics of a database include:
Storing structured or semi-structured data
Security features to prevent unauthorized use
Search capabilities
Backup and restore capabilities
Efficient storage and retrieval of data
Support for query languages
Some common uses for databases include:
Streamlining and improving business processes
Simplifying data management
Fraud detection
Keeping track of customers
Storing personal data
Securing personal health information
Gaming and entertainment
Auditing data entry
Creating reports for financial data
Document management
Analyzing datasets
Customer relationship management
Online store inventory
What Is a Data Warehouse?
A data warehouse is a larger storage location than a database, suitable for mid- and large-size businesses. Companies that accumulate large amounts of data may require a data warehouse to keep everything structured. Data warehouses can store information and optimize it for analytics, enabling users to look for insights from one or more systems. Typically, businesses will use data warehouses to look for trends across the data to better understand consumer behavior and relationships.
These specialized systems consolidate large volumes of current and historical data from different sources to optimize other key processes like reporting and retrieval. Data warehouses also enable businesses to share content and data across teams and departments to improve efficiency and power data-driven decisions.
The four main characteristics of a data warehouse include:
Subject-oriented: Data warehouses allow users to choose a single subject, such as sales, to exclude unwanted information from analysis and decision-making.
Time-variant: A key component of a data warehouse is the capability to hold large volumes of data from all databases in an extensive time horizon. Users can perform analysis by looking at changes over a period of time.
Integrated: Users can view data from various sources under one integrated platform. Data warehouses extract and transform the data from disparate sources to maintain consistency.
Non-volatile: Data warehouses stabilize data and protect it from momentary changes. Important data cannot be altered, changed or erased.
A data warehouse can also have the following elements:
Analysis and reporting capabilities
Relational database for storing and managing data
Extraction, loading, and transformation solutions for data analysis
Client analysis tools
Common use cases for data warehouses include:
Financial reporting and analysis
Marketing and sales campaign insights
Merging data from legacy systems
Team performance and feedback evaluations
Customer behavior analysis
Spending data report generation
Analyzing large stream data
What Is a Data Lake?
The next step up in data storage is a data lake. A data lake is the largest of the three repositories and acts as a centralized storage system for organizations that need to store vast amounts of raw data in their native format, including:
Structured
Semi-structured
Unstructured
As the name suggests, a data lake is a large virtual “pond” where data is stored in its natural state until it’s ready to be analyzed. Data lakes are also unique because they are flexible — they can store data in many different formats and types, enabling businesses to utilize them for real-time data processing, machine learning, and big data analytics.
Data lakes solve a common organizational challenge by providing a solution to managing and deriving insights from large, diverse datasets. They allow businesses to overcome the obstacles of traditional data storage and efficiently and cost-effectively analyze data from many sources. Data scientists and engineers can also use data lakes to hold a large amount of raw data until they need it in the future.
Several key characteristics of a data lake include:
Scalability as data volume grows
Data traceability
Comprehensive data management capabilities
Compatibility with diverse computing engines
Some use cases for data lakes include:
Ensuring data integrity and continuity
Backup solutions
Data exploration and research
Centralized data repository
Archiving operational data
Storing vast amounts of big data
Maintaining historical records
Internet of Things data storage and analysis
Real-time reporting
Providing the data needed for machine learning
Core Differences Between Databases, Data Warehouses, and Data Lakes
The most noticeable difference between these three types of data solutions is their applications. For example, you would have much more storage for raw data in a data lake vs. a data warehouse.
Alternatively, databases are typically used for relatively small datasets, while data warehouses and data lakes are more suited to large volumes of raw data across a wide range of sources. However, other factors contribute to the distinction among these data storage options.
1. Structure and Schema
Databases work best with structured data from a single source because they have scaling limitations. They have relatively rigid, predefined schemas but can provide a bit of flexibility depending on the database type. Data warehouses can work with structured or semi-structured data from multiple sources and require a predefined or fixed schema when data flows in. Data lakes, however, can store structured, semi-structured, or unstructured data and do not require a schema definition for ingest.
2. Data Types and Formats
Databases are ideal for transactional data and applications that require frequent read-and-write operations. Data warehouses are suitable for read-heavy workloads, analytics, and reporting. Data lakes can store large amounts of raw, natural data in many formats. If comparing a data lake vs. a database, you’d have much more flexibility for different types of data in a data lake.
3. Performance and Scalability
Scalability is limited with databases, making them more suitable for small to medium-sized applications and moderate data volumes. It is challenging for databases to adapt to new types or formats of data without significant reengineering.
Data warehouses can provide a high level of scalability and optimized performance for large amounts of structured data. While they can accommodate changes in data structures and sources, it requires intentional planning. Data lakes offer the most flexibility and scalability for organizations, allowing them to store data in various formats and structures. Data lakes can also accommodate new data sources and analytical needs.
4. Cost Considerations
The cost of data storage plays an important role in deciding which solution is best for your needs. Databases offer cost-effectiveness for most small- to medium-sized applications and can scale up and down to meet changing needs.
Data warehouses provide more scalability and improved performance, but they often require significant investment in software and hardware. Data warehouses also tend to incur higher storage costs than databases. For this reason, when comparing a data lake vs. a data warehouse solution, you may get more for your investment in a data lake. Data lakes are the most cost-effective option for organizations looking to store vast amounts of raw data.
Advantages and Disadvantages of Each Solution
To further understand which data storage solution is right for your business, let’s take a look at the pros and cons of databases, data warehouses, and data lakes.
Databases
Databases can improve operational efficiency and data management processes for many small and mid-size businesses. Some key advantages of using databases include:
Removing duplicate or redundant data
Providing an integrated view of business operations
Creating centralized data to help streamline employee accessibility
Improving data-sharing capabilities
Fostering better decision-making
Controlling who can access, add, and delete data
Using databases can also come with several drawbacks, such as:
Potential for more vulnerabilities
More significant disruptions or permanent data loss if one component fails
May require specialized skills to manage
Can lead to increased costs for software, hardware, and large memory storage needs
Data Warehouses
Data warehousing can help your organization make strategic business decisions by drawing valuable insights. Advantages of a data warehouse include:
High data throughput
Effective data analysis
Consolidated data in a single repository
Enhanced end-user access
Data quality consistency
A sanitization process to remove poor-quality data from the repository
Storage of heterogeneous data
Additional functions such as coding, descriptions, and flagging
High-quality query performance
Data restructuring capabilities
Added value to operational business applications
Merging data to form a common data model
When working with a data warehouse, you may experience some disadvantages, including:
Reduced flexibility
The potential for lost data
Data insecurity and copyright issues
Hidden maintenance problems
Increased number of reports
Increased use of resources
Data Lakes
Data lakes are capable of handling large amounts of raw data, which means they can be an attractive option for organizations that require scalability and advanced analytics. Other key advantages of data lakes include:
An expansive storage space that grows to your needs
Ability to handle enormous volumes of data
Easier collection and indefinite storage of all types of data
Flexibility for big data and machine learning applications
Capable of accommodating unstructured, semi-structured, or structured data
Ability to adapt and accept new forms of data from various sources without formatting
Eliminate the need for expensive on-site hardware
Reduced maintenance costs
Capability to integrate with powerful analytical tools
Some potential drawbacks of data lakes may include:
Complex management processes
Security concerns due to storing sensitive data
Potential for disorganization
More vulnerable to becoming data silos
Choosing the Right Data Storage Solution
Now that you know the difference between a data lake, a data warehouse, and a database, it’s time to find a solution that fits your organization’s needs. Here’s what to consider:
Your data requirements: Not all data storage solutions can support all types of data. For example, if your data is structured or semi-structured, you may prefer a data warehouse. However, a data lake supports all types of data, including structured, semi-structured, and unstructured.
Current storage setup: How do you store your organization’s data? Depending on where and how you store it, you may or may not have to move data to a new storage solution. For instance, a data lake may not require you to move any data if it’s already accessible, which means your organization can skip the process.
Industry-specific considerations: You’ll need to consider the primary users of the data. For example, will a data scientist or business analyst need access to the data? Do you need it for business insights and reporting? Understanding your unique needs can help you narrow down which storage solution is best.
Primary purpose: In addition to your industry-specific needs, consider the main function of your data storage solution. For instance, databases are often used for transactions and sales, while data warehouses are more ideal for in-depth analytics of historical trends and reporting. Because databases and data warehouses serve different purposes, some organizations choose to use both to address separate needs. Data lakes, alternatively, are suitable for large-scale analytics and big data applications. If your organization hosts large amounts of varied, unfiltered data, a data lake may be the best option.
Future Trends and Considerations
Modern data storage continues to advance and evolve. Data lake solutions, in particular, have become vital to many organizations for their unparalleled flexibility in data management. Looking to the future, organizations can expect the integration of data lakes to become more advanced with the help of digital technologies like artificial intelligence and machine learning. These emerging trends suggest promising enhancements in threat detection, data management and security, and predictive analytics.
Adopting a data lake for your business can help instill a forward-thinking approach to data management and storage. Addressing common issues like poor scalability and the constraints of a fixed schema can help your organization shift to a more convenient way to manage diverse data types.
JumpStart Your Data Journey With Kopius
Data storage and organization are unique to every business. While a database or data warehouse may suit your needs for a while, there’s no telling what your needs will be in the future.
When you partner with Kopius, you benefit from data solutions that drive strategic outcomes from one accessible location. Gone are the days of struggling to keep up with the latest transformations to power growth. Today, setting up a data lake is easier than you think.
With data lake capabilities from Kopius, you can make decisions faster, yield actionable reports and store data in all types and formats. Our turnkey solutions are designed to meet your needs, whether you require robust access control or oversight and support for your data lake.
