With the exponential increase in data generation from almost every digital source, extracting and leveraging this data can be quite challenging for businesses of any size. Hence, companies look out for unified platforms where they can easily extract data from different sources present in various formats and use this data for analysis or perform other tasks. Such a platform serves as the ultimate storehouse for all data, transforms it into a single source of truth, and allows the scaling of complex analytics operations that turn data into valuable insights. These platforms are referred to as Data Platforms. 

While enabling a spectrum of users, from data analysts to corporate executives, data platforms must be able to handle data sets of higher velocity, variety, and volume while allowing users to explore, track, and analyze the data in order to make informed decisions. This guide will help you understand how data platforms work and what factors you should consider while building or choosing a data platform. Moreover, you will explore the different layers of the data platform. At the end of this guide, you’ll also learn about the various benefits and limitations of these platforms. Before you jump onto these sections, let’s get an overview of what exactly are data platforms and how are they different from Big Data platforms.

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What is a Data Platform?

As discussed above businesses frequently struggle with data management issues, such as consolidating different data types that are stored in different silos, data lakes, or on-premise servers. They look for a solution that offers a one-stop shop where unifying varied data, including structured and semi-structured data, is a crucial component. This is where a data platform comes into the picture.

A data platform often referred to as a data management platform, is an integrated framework that encompasses the features of a data lake, data warehouse, data hub, and Business Intelligence (BI) platform. Without a data platform, each component is typically handled by a different tool or collection of tools to make data flow from source to end user in a complex environment. A data platform saves your day by consolidating numerous solutions into a single tool, making the final product much easier to manage. This unified platform provides cost-effective, scalable, and secure real-time business insights through analytics.

In order to adapt to new technologies and the changing requirements of today's data teams, a modern data platform is built to be democratic, proactive, scalable, and flexible. It serves as the technology foundation for connecting and running data tools and applications. It offers an all-in-one platform for data capture, storage, processing, and analysis required for users to make data-driven decisions.

Data Platform vs Big Data Platforms

The term "Big Data" first gained popularity in the 1990s, when the amount of data generated began to increase exponentially. Now, there are billions of internet users on the planet and new data is generated by each activity they do online. Hence, businesses in every sector want to use this data to track inventory, manage resources, collect customer information, and lots more. Due to this phenomenal growth of data, any data platform that can meet these current data and organizational expectations can be categorized as a Big Data Platform.

A Big Data platform is an integrated one-stop computing solution for big data management that integrates a variety of software platforms & applications. Enterprises are increasingly using big data platforms because of their effectiveness in managing data to collect a tonne of data and transform it into organized, usable business insights. Currently, there is a tonne of big data platforms that are both open source and commercially available, all offering various features and advantages.

How does Data Platform Work?

As you learned above that data platforms help centralize your data to further make it useable for other processes. In this section, you will get an overview of how the data platform works. The following stages can be used to categorize the workflow:

1. Data Collection: Data is extracted by data platforms from a variety of sources, including sensors, weblogs, social media, data sources, SaaS sources, and other databases to name a few.
2. Data Storage: Once the data has been gathered, it is kept in a repository like Google Cloud Storage, Amazon S3, Amazon Redshift or Hadoop Distributed File System (HDFS).
3. Data Processing: Filtering, cleaning, standardizing, manipulating, transforming, and aggregating the data are a few examples of the tasks involved in the data processing stage. Distributed processing frameworks like Apache Spark, and Apache Storm, or third-party ETL tools such as Fivetran can be used for this. Tools like Fivetran will help automate the first three stages and also provide real-time monitoring. So you don't have to worry about your data workflow.
4. Data Analytics: After processing, data is then analyzed using analytics tools and methods like data visualization, predictive analytics, and machine learning algorithms. Companies often use Business Intelligence tools such as Looker studio, Tableau, Microsoft Power BI or pre-built machine learning models offered by AWS, and Azure to analyze their data to generate valuable business insights.
5. Data Governance: The correctness, completeness, and security of the data are ensured by data governance, which includes data cataloguing, data quality management, and data lineage tracing.
6. Data Management: Data platforms offer management features that let businesses create backups, and archive their data.

These steps are intended to generate actionable business insights from unstructured data extracted from various sources, including CRM, ERP, files, databases, etc. This processed data that has been saved in a unified environment can be leveraged to deliver reports as well as be used for building machine learning models.

What are the 6 layers of a Data Platform?

The modern data platform typically consists of six fundamental layers, which are detailed below:

• Data Sources
• Data Ingestion Layer
• Data Processing Layer
• Data Storage Layer
• Data Analytics Layer
• Data Visualization Layer

1) Data Sources

The data platform does not produce data on its own; rather, it receives data from various sources and processes it before feeding it into the platform. Structured, semi-structured, unstructured, and streaming data are the 4 types of data that can be loaded onto the data platform.

ERP and CRM systems are among the most popular types of data sources for data platforms. This data is already stored in databases and is referred to as structured data since the tables follow a rigid structure of columns and datatypes to define the data. Text files with specific formats, such as XML, JSON, and CSV, are additional crucial sources. Since these file types have a partial structure and are more flexible than the structured data contained in tables, these types of data sources are referred to as semi-structured.

Also, it's probable that some unstructured data will need to be housed in the data platform. They can be plain-text files without a pre-established data model or schema, like log files, or other file types like photos, videos, or documents. Moreover, streaming data can be used which may be produced by sensors, IOT gadgets, live broadcasts, etc. The difficulty with streaming data is that it must be recorded, processed, and stored as soon as it is received, which places time constraints on the job. Hence, it is crucial to define your sources in this layer as these sources can have an effect on all the data platform layers ahead.

2) Data Ingestion Layer

Data ingestion is the process of extracting data from various data sources to a single location. In the context of Extract Transform Load (ETL) and Extract Load Transform (ELT), this is frequently referred to as the extraction stage.  The data can then be used for additional processing and analysis or for retaining records. Some of the common methods for extracting data are:

• Full Data Extraction: Data is extracted all at once from the selected data source. Given that it is not necessary to know which data has been changed, this is the easiest way to implement it.
• Incremental Data Extraction: It only extracts the altered records. To use this method, the source system must be able to identify which records have undergone changes in order to ensure that only those are extracted.

Some of the popular data ingestion tools include Fivetran, Apache Kafka, Google Cloud Data Flow, and many more. Fivetran is a cloud-based automated ETL (Extract, Transfer, Load) tool that assists in moving data from different sources to data storage, like a data warehouse or a database. To consolidate their data, users can connect to more than 100 data sources by leveraging Fivetran's powerful connectors. It quickly adjusts to the API and schema changes to ensure data consistency and integrity. Even though there are numerous automatic ingestion tools in the market today, some data teams prefer to create their own custom frameworks or even write their own custom code in order to ingest data from internal and external sources.

3) Data Processing Layer

The data processing Layer is responsible for coordinating the process of loading ingested data to the storage and transforming the data so that it can be deposited in the storage layer. To better serve the needs of the data platform, the data model at the storage layer is frequently modified. In this scenario, the data is transformed by the processing layer from the source data model to the storage layer's data model.

Determining whether to use batch processing versus real-time processing is another crucial issue that needs to be handled at this layer. Since data teams and analytics tools need current data, real-time processing is important. On the other hand, batch processing works well when a data delay is acceptable. 

The data processing layer should be capable of carrying out certain operations, such as reading data from storage in batch or streaming workflows and applying transformations, support for widely used programming languages and querying tools and scaling up to handle an expanding dataset's processing requirements. The various transformation operations typically include data cleaning, data formatting, data normalization, and many more. For carrying out these transformation operations on the data, a wide range of tools are available on the market, including tools where manual intervention is required like spreadsheets, OpenRefine, Google DataPrep, and automated ETL tools like Fivetran, Stitch, Talend, etc which handle these transformations on their own. In addition, a number of libraries and packages specifically designed for data processing are also available in Python and R, which big corporations use to build their own data transformation models from scratch.

4) Data Storage Layer

Data is stored at the storage layer once it is ingested from the data sources and transformed in the processing layer. The data storage layer helps in disaster recovery, data archiving, making the data accessible, and protecting the data from failures, disasters, or user errors.
Data warehouses, data lakes, and data lakehouses are just a few examples of cloud-native solutions that have emerged as a result of businesses moving their data platforms to the cloud. These solutions offer more accessible and affordable options for storing data than many on-premises solutions.

Depending on the sort of storage you require, a variety of technologies can be employed to store the data, each with pros and disadvantages of their own. The most popular storage solutions are:

• Relational Database Management Systems (RDBMS): These databases stored structured data and are widely employed by Online Transaction Processing (OLTP) systems, such as ERP or CRM platforms.
• Massive Parallel Processing (MPP) Database: This is a particular kind of relational database, but the key distinction is that a portion of the data is processed on each of several computers with linked storage. Since these databases are made to handle queries that require enormous volumes of data and are not effective at handling numerous little requests, they are exclusively suited for OLAP solutions and are not appropriate for OLTP.
• NoSQL Databases: NoSQL databases don't use tables to organize data like relational databases do. They were introduced to address some of the relational database shortcomings in terms of scalability and data model flexibility.
• Hadoop Distributed File System: This storage method uses a distributed file system, which distributes files among a number of machines with accompanying storage to speed up read and write operations. The concept behind this storage option is to keep the cost of storing enormous volumes of data to a minimum by using cost-effective servers.
• In-Memory Databases: Here, the data is stored in the main memory of the machine. In-memory databases are highly quick, but also quite pricey. As a result, you should use these only when there is less data, efficiency is crucial, and the data is often requested.
• Cloud Storage: It is a storage solution capable of holding any type of data, including files and tables in the cloud. Cloud storage systems provide the flexibility to select from a variety of protocols depending on how quickly, securely, and reliably the data should be retrieved.

5) Data Analytics Layer

If your employees can't use the data you have processed in your data platform, it doesn't help your business much.  As a result, the data analytics layer's goal is to create and run analytical models on the data, to make it easy for the end user to understand it. For this to be successful, it is crucial that the source data is properly prepared and cleaned by the above layers.

Many techniques can be used to perform data analytics. The majority of traditional analytics is carried out by feeding reporting or dashboarding tools with data that is kept in a relational database. More sophisticated analytics techniques, such as predictive monitoring, machine learning, and big data analytics, are frequently used for diagnostic, predictive, prescriptive, or automated analytics. Moreover, cognitive analytics typically leverages image recognition to identify persons or uses natural language models to identify emotions from human text or speech. On the other side, ad-hoc analytics typically uses self-service BI or ad-hoc queries to obtain answers to specific problems.

The ideal data analytics tool to choose will rely on your use case, the type of analytics required, and the best methodologies to implement. There are many factors to take into account while making this decision.

6) Data Discovery & Visualization Layer

Data discovery includes gathering and analyzing data from multiple sources. It is frequently used to comprehend the trends and patterns revealed by the data. Due to its ability to integrate disparate data sources for analysis, data discovery is occasionally equated with business intelligence.

Data can be visualized using dashboards and reports after it has been stored and/or processed to create accurate information that can be used to obtain insights and direct company decisions. Reports and dashboards with extensive self-service features are frequently requested by business users. However, today data storytelling is one of the great ways to successfully visualize data. It is a method of sharing data insights that combine data, graphics, and a narrative. Businesses can leverage their current analytics applications by utilizing various integration libraries present in the market. Some of the popular data visualization solutions used by businesses include Looker Studio (formerly Google Data Studio), Tableau (acquired by Salesforce) and Power BI (by Microsoft) among many others.

What are the Types of Data Platforms?

Now that you have a basic understanding of the data layers present in a data platform, let’s take a glance at the different types of data platforms available.

1) Enterprise Data Platform

Access to a company's data assets is consolidated through an Enterprise Data Platform (EDP). It can efficiently access information for both internal corporate applications and communications with the outside market while accurately determining, and seamlessly integrating with them.

Enterprise data platforms are typically comprised of conventional data sources and reside in on-premises or hybrid environments. An EDP might contain OLTP databases, data warehouses, and a data lake. Tools and methods for data collection, preparation, and analytical reporting are also included in EDPs. Data from all systems are consolidated into a relevant structure and format.

An enterprise data platform ensures that business users can access the data housed within the EDP to make better decisions that will enhance the process and promote business growth for data-driven enterprises. It provides a single, unified picture of the data that can be easily manipulated and analyzed using a number of tools and techniques to suit the needs of the business. Both the complexity and difficulty of IT integration are considerably decreased.

2) Cloud Data Platform

A general phrase for data platforms constructed solely using cloud computing and data repositories is referred to as  "cloud data platform." A Cloud Data Platform, for instance, might have limitless object storage, managed relational and NoSQL databases, MPP data warehouses, Spark clusters, Analytics Notebooks, and lots more. Enterprise data platforms and cloud data platforms can coexist with modern data platforms. For instance, the ERP, Supply Chain Management, CRM, and Finance data stores may all be included in an organization's EDP.  All of those services might be provided by a cloud data platform.

Several cloud and database companies have developed products that let users store and process enormous amounts of data in various formats on their platforms. A feature of public cloud suites is cloud databases. Every aspect of these relational and non-relational databases, including the software, infrastructure, high scalability, and backup, are maintained as a service on the cloud. Clients don't need to bother about database operations as the use of correct data management techniques, such as maintaining database architecture and providing appropriate security standards, are ensured with the use of these platforms. The majority of cloud data platforms also make it possible to use data for tasks other than merely storing it, such as sharing and analysis.

3) Modern Data Platform

Today, the enterprise data platform naturally evolved into a modern data platform. In addition to EDP capabilities, the modern data platform offers a wide range of agility and other robust features. This data platform was typically developed out of the need to store and handle various types and volumes of data. 

Modern data platforms, in addition to enterprise data platforms’ batch processing tasks, can support data streaming in real time. Moreover, the ability to create machine learning applications, execute complex operations and natively analyze structured, semi-structured, or unstructured data at scale can be supported by this platform. These data platforms frequently leverage cloud technology due to the cloud benefits such as flexible and affordable pricing models, elastic scalability, and customizable managed services.

4) Data Analytics Platform

A specialized data platform for data analytics is known as a data analytics platform, commonly referred to as a Big Data analytics platform. Users can execute intricate queries on enormous volumes of data in any format, and the resulting analysis and exploration can be used to produce valuable insights.

Data analytics platforms frequently combine multiple Big Data tools and utilities in one location while taking care of performance, scalability, and security in the background. The majority of the time, these solutions are provided as Data-as-a-Service and are a component of a cloud suite or SaaS solution. They have many more features than simply using conventional SQL on structured data. Usually, operational data from enterprise, modern, or other data platforms are aggregated within data analytics platforms to analyze the data.

5) Customer Data Platform

Client-specific data is the only thing that a Customer Data Platform (CDP) focuses on. It combines customer information from several sources, including CRM, transactional systems, social media, emails, websites, and eCommerce businesses. The combined information creates a comprehensive user profile that can be applied to marketing and other business endeavors like behavoir segmentation.

Contrary to a Customer Relationship Platform (CRM), a customer data platform can compile both known and anonymous customer data from many sources.  Although CDPs can handle a variety of use cases including omni-channel marketing, audience targeting, and a 360-degree overview of a customer, its primary value proposition primarily aligns with marketing teams.

Despite the fact that CDPs have many types and a plethora of features, they all work to support businesses in achieving the following objectives:

• Organize, centralize, and safeguard all kinds of customer data.
• Create profiles of customer behavoir and customer journeys that users can edit and update in almost real time.
• Improve your knowledge of both current and potential customers.
• Boost operational effectiveness that is customer-focused.
• Enhance marketing initiatives with tailored campaigns.

Understanding Data Platform Architecture

A data architecture primarily serves as a framework for the data environment of a company. Data platforms differ from data architectures. A data platform is a system that reads, transfers, analyses, and validates data for end users, whereas a data architecture is a plan for ingesting, storing, and delivering data.

Solid architectural principles are becoming more and more crucial with the advent of technologies like edge computing and the Internet of Things (IoT). This highlights the significance of a strong data architecture, which serves as the foundation of a data-driven company and provides a strong infrastructure that can scale to meet changing data needs.

Below we have listed three key features that are taken into consideration when creating a modern data platform architecture:

• Scalable & Flexible: Data architectures are designed to regulate the flow of data inside an enterprise so that each business unit may quickly obtain the information that is necessary for achieving its objectives. As business requirements and data sources keep changing, the data platform architecture should scale and adapt to such changes without difficulty.
• Automation & Intelligence: In order to reliably organize and deliver data to its destination, a data architecture should automate data ingestion and distribution as much as possible. This reduces the maintenance required. Together with automation, a data architecture should make use of machine learning and artificial intelligence techniques to alert users of any issues, correct inaccurate data, and continuously enhance its capacity to anticipate user needs.
• Data Governance & Security: All of the aforementioned characteristics must be balanced with security. Any organization, its clients, and their tools must be secured, thus a strategy for data security as the data platform architecture scales is quite essential. Strong data encryption techniques and data lifecycle management can be used to maintain rigorous security and comply with privacy standards.

Key Advantages of Data Platform

A unified data platform is the first step in gaining the most value from your data, regardless of whether your goal is to comprehend the complex behaviors of your consumers, seek to resolve challenging problems, or just wish to use all of the information you already have to make decisions. A data platform offers a variety of advantages, some of which are listed below:

• Enhanced Data Collaboration: Data is liberated from silos and made accessible throughout the company. A data platform facilitates more coordinated decision-making inside an organization by integrating data from multiple sources. It enables firms to compete on data as an asset by standardizing both unstructured and structured data.
• Speed up Time to Value: The creation of value from your data can be slowed down by a variety of factors, including different tools used, various data storage platforms, and the use of batch workflows. In addition, the company cannot benefit from the current data exploitation techniques because of outdated data tools and fragmented data present in different silos. This problem is made worse by high licence costs and the challenge of finding skilled employees in the dwindling market for such outdated tools. Hence, a modern data platform will save you from additional time and effort by enabling you to apply cutting-edge tools and expertise, and opening up a larger pool of potential new hires. The time to value for a data project is reduced since the data platform handles all the major tasks such as data governance, ETL, and analytics.
• Quick Data Ingestion & Analysis: Obtaining your data in real time is the biggest challenge to leveraging it to its full potential. With the help of a data platform, fewer systems are required for end users to interact with. It provides for the seamless integration of data ingestion & analysis which enables quick access. Leading data platforms are able to ingest and process a large number of decisions per second, allowing businesses to scale data collection, automation, and decision-making.
• Increased Scalability: Data platforms can be upgraded or downsized as necessary. Using third-party data platforms, it is simple to adjust the subscription as needed if only a small amount of data needs to be saved in one month but a lot is anticipated the following. Similar to this, service plans can be customized to meet different needs of data analysis.
• Robust Data Governance: A data governance plan is necessary for valuable data. Without this approach, it is possible to gather inaccurate or unnecessary data. An organization's data governance policy, including the types of data to gather and who can access it, can be better managed with the use of a data platform. Data warehousing and the use of data platforms both aid in protecting against data loss and security breaches. These platforms typically allow for backups across geographical areas, lowering the chance of data loss due to a major incident like a fire or flood. Moreover, this replication can take place in real-time, guaranteeing that the backup is constantly updated.
• Cost-Effective: Data platforms require very little start-up costs and can readily be planned for as part of ongoing expenses rather than making significant capital expenditures. In addition, data platforms are good at predicting monthly spending plan and typically include tools for doing so.

Limitations of Data Platform

Businesses across all sectors are employing data platforms in their use cases to benefit from their data. Although a data platform can be an effective tool, you need to be conscious of potential issues and challenges. There are a few limitations associated with data platforms as discussed below:

• Privacy issues: Third-party data is essentially what powers data platforms. While solving the problem of limited data, such data also raises privacy concerns. The GDPR mandates that you obtain users' consent before collecting and using personal data. When you take into account the permission flows between third-party vendors, this process can be really challenging.

• Lack of Data Quality: By associating cookies with a specified taxonomy that takes into account user activities and context, data platforms can reach a wider audience. The taxonomy, however, is predefined. In other words, it is based on strict guidelines for data collection, which can result in low-quality data. The quality of the results will be poor if poor-quality data is imported into the data platforms. For instance, the majority of third-party data may be outdated, making it difficult to determine factors like consumer intent. Also, the data may be ambiguous or devoid of specific characteristics that would enable you to group customers into the appropriate audience. Additionally, adopting these taxonomies powered by third-party data may prevent you from learning more about the data sources.

• Steep Learning Curve: While data platforms are quite robust and valuable solutions, implementing and adopting them in your business may not be successful with your team as it's possible that the technology and skill set available prevent the integration of a data platform into your company. Hence, it would first need the appropriate technical and subject-matter understanding. Furthermore, your team may not be able to use a data platform due to its complexity. That would imply that mastering its use would involve a steep learning curve.

All data platform solutions offer significant advantages, despite having a few minor drawbacks. Therefore, you should know how to choose or build the right platform for your business use case. Read the next section to discover some important factors that will help in your decision-making.

Critical Factors to Consider while Choosing a Data Platform

It can be challenging to choose the best data platform. Finding the one that best satisfies your demands requires extensive research of your possibilities. Finding the market's top data platform generally is not the main goal in the end. Instead, it involves putting in place the data platform that enables you to accomplish your unique business goals in relation to how you wish to use your data.

Let’s take a look at the factors below that you should keep in mind while choosing the right data platform for your organization.

• Outline your Business Objectives: List the fundamental objectives for the various business use cases. Identify why you need the data platform, and what requirements the platform should perform or be able to perform.
• Choose between On-premises, Cloud or Hybrid: Businesses must choose whether to adopt open-source software or proprietary alternatives. Whether you handle your data on-site, through a cloud provider, or a combination of both depends on a variety of factors. These considerations include the need for security and compliance, the price of various platforms, the skills and tasks you want to keep in-house and those you would obtain from suppliers, and more. Once you've established the fundamental prerequisites, it's time to look into and test potential vendors. Set up the precise metrics and performance that you require.
• Check the Scalability: A data platform needs to function at today's scale and be flexible enough to accommodate the inescapable expansion of your data repositories. One of the key reasons why cloud-based data systems are being adopted more widely is that they easily scale with your data expansion.
• Take a look at Platform’s Flexibility: In addition, to scalability, you need to make sure that the platform can handle a variety of use cases and allows you to customize or add new features or tools to the existing platform.
• Easy-of-Use: Is the platform you're thinking about easy to set up and deploy for users of different skill levels? The data platform should allow every employee in your company, from IT experts to non-technical staff, to be able to work with that data.
• Does it Guarantee Security & Compliance: In order to avoid data breaches that can put you in a tough situation, organizations must make sure that their data is secure. As a result, you must keep in mind that your data platform has strong security features integrated into it while finalizing your data platform. It is imperative that your data management platform adheres to the standards and regulations set forth by the regulatory organizations of your country.
• Intelligent Data Monitoring: Technology advancements, particularly in the areas of machine learning (ML) and artificial intelligence (AI), have opened up new possibilities for businesses of all sizes to gain insights from their data. Your data platform should be able to alert your team in case of any major issues while using its intelligence to solve common minor issues on its own.

Conclusion 

The enormous growth in data sources and volume as well as the various data requirements of different users present serious challenges. To analyze and manage data, businesses utilize a range of tools. This is where a data platform comes into the picture. It makes it possible for data to be consolidated and gathered from different sources and further transform, and distribute to end-users, applications, or use for analysis tasks. 

To conclude, data platforms have become a necessity be it any business or sector. This guide helped you gain an in-depth understanding of data platforms, their various types, architecture and the various layers it’s made up of. In addition, you learned about the benefits as well as the challenges associated with the data platforms. At the end of this guide, we discussed the critical factors to be kept in mind while narrowing down your data platform choices.  

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