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In recent years, there has been a growing interest in the use of synthetic data for various applications, such as machine learning and data analytics. According to Gartner, by 2030, synthetic data use will outweigh real data in AI models.
In this article, we will explore:
what is synthetic data and how it is created
what are the benefits of synthetic data over real data
what are some of the challenges with using synthetic data
which type of data should be used for specific applicationsWhat is synthetic data? How is it created?
Synthetic data is data that has been artificially created by computer algorithms, as opposed to real data that has been collected from natural events.
Although there are other ways to generate synthetic data, AI-generated synthetic data is produced by AI that is trained on complex real-world data, by the power of deep learning algorithms. The merit in using generative AI is that it is capable of automatically detecting patterns, structures, correlations, etc. within real data, and then learning how to generate brand new data with the same patterns. You can see the structural similarity in Figure 1 below.
Figure 1. (Source: UK Government)
One popular method is to generate data using a computer algorithm that mimics the behavior of real-world data. This approach can be used to create synthetic data sets that are similar to real data sets in terms of their distribution and variability. Another common method for creating synthetic data is to use a random number generator to create data that is uniform and has no correlation.
For more on what is synthetic data and its benefits, you can check our article.The benefits of synthetic data over real data
There are several benefits of using synthetic data over real data. Below we listed 8 ways synthetic data can be useful.
1. Overcomes regulatory restrictions: The most important benefit of synthetic data over real data is that it avoids regulatory restrictions on real data. Synthetic data can replicate all important statistical properties of real data without exposing the latter, eliminating any concern about privacy regulations. This feature thus further enables:
Privacy preservation: It is hard to sustain privacy in classic anonymization methods while preserving the usefulness of the real dataset. You have to choose either protecting the privacy of the people while diminishing the effectiveness of that data or getting usefulness while renouncing privacy. With synthetic data, the privacy/usefulness dilemma is resolved since there is no real data that you must protect against leaking.
Resistance to reidentification: Real data removes certain information to satisfy anonymization. Yet, reidentifying the data source is still highly possible. As a study shows, sharing only 3 bank transaction information per customer, with the merchant and the date of the transactions, makes 80% of customers identifiable.
Aptitude for Innovation and Monetization: As there are no privacy concerns for synthetic data, it is possible to share these datasets with third parties for innovation research and to use them as a monetisation tool.
2. Streamlines simulation: Synthetic data enables the creation of data to simulate conditions that have not yet been encountered. Where real data does not exist, synthetic data is the only solution. For instance, automotive firms may not gather real data for all possible situations to train smart cars.
3. Avoids statistical problems: Synthetic data is immune to some common statistical problems. These can include item nonresponse, skip patterns, and other logical constraints. For example, a synthetic data generation program could be designed to ensure that all items in a survey are answered, and that there are no skip patterns in the responses. This can be done by specifying the rules for generating the data, such as the possible response options for each item and the dependencies between items. By carefully designing these rules, the synthetic data can be generated in a way that avoids common statistical pitfalls.
4. Speeds up the process: Synthetic data can be generated much faster than real data can be collected, saving time and ensuring agility and competitiveness in the market.
5. Achieves higher consistency: Synthetic data can be more uniform and consistent than real data, which can be variable due to its natural origins. This uniformity makes synthetic data more suitable for performing accurate analyses on synthetic datasets.
6. Ensures easy manipulation: Synthetic data can be more easily manipulated than real data in a controlled way, which can be difficult to alter without compromising accuracy. Therefore, it allows for more precise and controlled testing and training of machine learning models, and it can be generated in large quantities with specific characteristics and biases. This can be useful for improving the performance of machine learning algorithms in a variety of applications.
7. Increases cost-effectiveness: Synthetic data can be more cost-effective than real data. Of course, creating synthetic data is not free. The main cost of synthetic data is an upfront investment in building the simulation. However, real data enforce timely and financial costs every time a new data set is required or an existing one is revised.
8. Facilitates AI/ML training: Synthetic data is more enriching for teaching AI/ML models as it has no regulations restricting real data. Also, it has a higher capacity to create more data, feeding AI much more to learn. For more detail, check our article on the use of synthetic data to improve deep learning models.Some challenges with using synthetic data against real data
Besides a variety of benefits, there are some challenges with using synthetic data.
Biased or deceptive results: Synthetic data can be misleading, limited or discriminatory due to its lack of variability and correlation.
Lack of accuracy: Another challenge with synthetic data is that it is often created using a computer algorithm, which may not always be accurate. As a result, synthetic data can occasionally produce inaccurate results.
Time-consuming steps: Relatedly, synthetic data requires additional verification steps, such as comparing model results with human-annotated, real-world information. Such efforts take time to complete and prolong the projects.
Losing outliers: Synthetic data may not cover some of the outliers present in the original dataset because it can only mimic but not replicate real data. However, outliers can be relevant for some research.
Dependency on the real data: Synthetic data quality often depends on the real model and the dataset that have been developed for creating synthetic data. Without a desirable and qualitative real dataset, various synthetic datasets that are generated in huge amounts by using the original dataset will end up functioning ineffectively and sometimes even incorrectly.
Consumer skepticism: As synthetic data use increases, businesses can face consumer skepticism, such as questioning the credibility of the data for reaching conclusions and making products. Consumers might demand assurance for the transparency of the data generation techniques and the privacy of their information.
Despite these challenges, synthetic data remains an important tool for data analysis. When used correctly, synthetic data can provide valuable insights into the behavior of real-world data.Which type of data should be used for specific applications? Synthetic or Real?
As we discussed in the section on the benefits of synthetic data, there are various application areas it can be used, while it is impossible to use real data.
For example, synthetic data can be used in radioactive data sets. The term “radioactive” is often used to describe data that is constantly changing and difficult to keep track of. This can be due to a variety of factors, such as the rapid growth of the dataset, the frequent addition of new data points, or the dynamic nature of the data itself. It is highly difficult to keep track of such data in a real data method.
On the other hand, it is better to use real data rather than synthetic data in cases where the goal is to reproduce the exact distribution of a real-world dataset. In such cases, it is often preferable to use the original dataset rather than a synthetic version.
In cases where the goal is to study the correlation between different variables in a dataset, it is often better to use real-world data instead of synthetic data, which typically does not exhibit any correlation.
Additionally, synthetic data can be difficult to interpret and may not accurately reflect the behavior of real-world data.
Ultimately, the type of data that should be used for a particular application depends on the specific needs of the analysis. When accuracy is key, then real-world data should probably be used. However, in cases where speed or consistency is more important than accuracy, then synthetic data may be a better choice.For more on synthetic data
If you want to gain more insight on synthetic data, its benefits, use cases, tools, you can check our other articles on the topic:
If you have questions regarding synthetic data and real data, feel free to contact us:
Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.
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Difference between Data Warehousing vs Data Mining
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Let us understand the Difference between Data Warehousing and Data Mining in detail.Key Features Data Warehouse
Subject-Oriented: A data warehouse is subject-oriented as it provides knowledge around a subject rather than the organization’s ongoing operations. These subjects include a product, customers, suppliers, sales, revenue, etc. A data warehouse focuses on modeling and analysis of data for decision-making.
Integrated: A data warehouse is constructed by combining data from heterogeneous sources such as relational databases, flat files, etc.
Time-Variant: The data in the data warehouse provides information concerning a particular period.
Non-volatile: Non-volatile means data, once entered into the warehouse, should not change.
Benefits of Data Warehouse
Consistent and quality data
More timely data access
Improved performance and productivityData Mining
Automatic discovery of patterns
Prediction of likely outcomes
Creation of actionable information
Focus on large data sets and databases
Direct marketing: The ability to predict who is most likely to be interested in what products
Fraud detection: Data mining techniques can help discover which insurance claims, cellular phone calls, or credit card purchases are likely to be fraudulent.Head to Head Comparison Between Data Warehousing vs Data Mining (Infographics)
Below is the Top 4 Comparison Between Data Warehousing and Data Mining:Key Differences Between Data Warehousing and Data Mining
Data Warehousing is the process of extracting and storing data to allow easier reporting. Whereas Data mining is the use of pattern recognition logic to identify trends within a sample data set, a typical use of data mining is to identify fraud and to flag unusual patterns in behavior. For Example, Credit Card Companies provide you an alert when you are transacting from some other geographical location that you have not used previously. This fraud detection is possible because of data mining.
The main difference between data warehousing and data mining is that data warehousing is the process of compiling and organizing data into one common database. In contrast, data mining is the process of extracting meaningful data from that database. Data mining can only be done once data warehousing is complete.
A data warehouse is a repository to store data.
Data warehousing is merely extracting data from different sources, cleaning it, and storing it in the warehouse. At the same time, data mining aims to examine or explore the data using queries.
A data warehouse is an architecture, whereas data mining is a process that is an outcome of various activities for discovering new patterns.
The data warehouse contains integrated and processed data to perform data mining during planning and decision-making, but data discovered by data mining results in finding patterns that are useful for future predictions.
The data warehouse supports basic statistical analysis. The information retrieved from data mining is helpful in tasks like Market segmentation, customer profiling, credit risk analysis, fraud detection, etc.
Data warehousing is the process of pooling all relevant data together, whereas Data mining is the process of analyzing unknown data patterns.
Data warehouses usually store many months or years of data. This is to support historical analysis. Data mining uses pattern recognition logic to identify trends within a sample data set.Data Warehousing and Data Mining Comparison Table
Below are the top comparison between Data Warehousing and Data Mining.
Data Warehousing Data Mining
It is a process that is used to integrate data from multiple sources and then combine it into a single database. It is the process that is used to extract useful patterns and relationships from a huge amount of data.
It provides the organization with a mechanism to store huge amounts of data. Data mining techniques are applied to data warehouses to discover useful patterns.
This process must take place before the data mining process because it compiles and organizes data into a common database. This process always takes place after the data warehousing process because it requires compiled data to extract useful patterns.
Engineers solely carry out this process. Business users carry out this process with the help of engineers.Conclusion
Data warehousing is a process that must occur before any data mining can take place. A data warehouse is the “environment” where a data mining process might take place.Recommended Articles
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Data analysis in business is playing a crucial role in decision making and more
The broad diversity of data generated by businesses includes significant insights, and data analytics is the key to unlocking them. Data analytics can assist a company with everything from tailoring a marketing pitch for a specific customer to recognising and reducing business hazards.Five Benefits of Using Data Analytics in Business Personalize the Customer Experience
Customers’ data is collected by businesses through a variety of channels, including physical retail, e-commerce, and social networking. Businesses can get insights into consumer behaviour and give a more personalised experience by employing data analytics to generate full customer profiles from this information.
Consider a retail clothing store that has both an online and offline presence. The company might evaluate its sales data in conjunction with data from its social media pages, and then design targeted social media campaigns to increase e-commerce purchases for product categories in which customers are already inclined.
Organizations can further optimise the customer experience by running behavioural analytics models on customer data.Inform Business Decision-Making
Data analytics can help businesses steer business decisions and reduce financial losses. Predictive analytics can anticipate what might happen in reaction to business changes, while prescriptive analytics can recommend how the firm should respond to these changes.
For example, a company can simulate changes to price or product offers to see how they will affect client demand. A/B testing of changes to product offers can be used to validate the hypotheses generated by such models. After collecting sales data on the updated goods, organisations may use data analytics approaches to evaluate the performance of the changes and visualise the results to help decision-makers decide whether to apply the changes across the company.Streamline Operations
Data analytics can help organisations enhance operational efficiency. Data collection and analysis of the supply chain can reveal where manufacturing delays or bottlenecks occur and assist identify where future problems may occur. If a demand projection indicates that a certain vendor will be unable to handle the volume required for the Christmas season, a business may supplement or replace this supplier to avoid delays in production.
Furthermore, many organisations, particularly those in retail, struggle to optimise inventory levels. Based on characteristics such as seasonality, vacations, and secular patterns, data analytics can assist in determining optimal supply for all of an enterprise’s services.Mitigate Risk and Handle Setbacks
In business, there are risks everywhere. Customer or staff theft, uncollected receivables, worker safety, and legal liability are among them. Data analytics may help a corporation evaluate risks and take preventative measures. For instance, a retail chain may employ a propensity model, which is a statistical method for predicting future behaviours or occurrences, to discover which stores are most prone to theft. The corporation may then use this data to determine the amount of security necessary at the stores, and also if it should divest from any places.Enhance Security
Data security issues affect all enterprises. By analysing and visualising relevant data, organisations can use data analytics to diagnose the reasons of previous data breaches. For example, the IT department can employ data analytics programmes to parse, process, and visualise audit logs in order to discover the path and origins of an incident.
In this post I’m going to look at getting real time data (RTD) into Power BI using a real time messaging service called PubNub.
This is intended for use with the Power BI online service, not Power BI Desktop.
Power BI provides a few different ways to get RTD : Push Data, Streaming Data and PubNub Streaming.
With this method, data is pushed, or sent, to Power BI and stored in a database that Power BI automatically creates.
Because the data is stored in a database, you can create reports using this data, as well as seeing the new data update in real time.
Streaming data is also pushed to Power BI but by default the data is not stored in a database.
You can tell Power BI to store this streamed data in which case you can run reports and analyse the data stored in the Power BI database.
But if you create the dataset as a ‘normal’ streamed dataset, Power BI only retains the data as long as it needs to display it on a tile. You can’t create reports for this data.
PubNub is a data streaming network (DSN) that provides a real time messaging service.
Put another way, it’s a high speed, low latency network that is built to allow you to easily send data from one place to another.
As with a lot of things that can be explained in a short, simple sentence, it is a very powerful concept.
Say you have an IoT device like a temperature sensor, or a GPS enabled vehicle, or maybe you’ve written an app that monitors your website’s uptime, anything that can record or generate data and has access to the internet, can use PubNub to send that data to anybody or anything that you want to send it to.
As we are streaming data to Power BI from PubNub, there is no database created in Power BI to store the PubNub data. We can visualize the data in tiles, but we can’t run reports against the data.
Pushing Data to Power BI Datasets
It’s worth mentioning at this point that there are a few ways to actually push your data into Power BI.
You can write your own applications (programs) that use the Power BI REST API.
This will require a good knowledge of programming and is no easy task.
If you use Azure Stream Analytics (ASA) you can configure Power BI to receive data from ASA but this is also a daunting task for the non-developer.
The easiest approach is to use PubNub. It’s pleasantly uncomplicated to do and although it does require some programming knowledge, or at least the will to give it a go, with the sample files I provide, hopefully you’ll be able to get your own test system up and running in no time.
First Things First – Setup a PubNub Account
To use PubNub you’ll need an account with them. They offer a free account for anyone interested in testing things out, so go and sign up now.
Once you are logged in, the first thing you should see is this which is telling you to go and get your API keys. You’ll need these to send and receive messages (data).
Please note that I have removed part of my Publish key to prevent naughty people sending data through my account. You should treat your own pub key carefully and don’t give it to anyone you don’t want sending data through your PubNub account.
When you have your API keys, you’re ready to start sending some PubNub messages.
Sending Data via PubNub
The idea is that you create a ‘channel’ along which you can send data.
A channel is just a name you give to something in PubNub. You don’t need to worry about what it really is or how it works, PubNub does all this for you. You’ll see later how easy it is to setup and use.
Anybody or anything that wants to receive this data can connect to the channel and listen for your messages, so long as you give them the subscribe key.
The data you send can be any JSON serializable data, which means you can send numbers, strings, arrays or objects.
You can send binary data (images, sounds) or any UTF-8 character, either single or multi-byte.
All of this requires a little programming but PubNub provides sample code and SDK’s (software development kits) for over 70 programming languages.
The code that sends the data, I’m calling the publisher. Remember the publisher can be anything. The computer monitoring the engine in your car. Your alarm system at home. If it has some data and can access the internet, you just need to hook it up with some code and you can send that data down a PubNub channel.
For my sample application I’m going to get the price in USD of Bitcoin, Ethereum and Litecoin from Crypto Compare, and send these prices down my channel where I’ll read them with another piece of code I’ll call my subscriber.
To begin with we need to insert into our code the keys we got earlier from our PubNub account.
The publishKey allows us to create a channel and send messages. The subscribeKey allows us to receive messages. The subscriber part of the code only needs the subscribe key.
A function called mainApp() calls the Crypto Compare website and gets the prices in USD for the crypto currencies. It does this every 2000 milliseconds. You can change this value if you wish.
When we have these prices, this code in the processRequest() function sends the prices down the channels.
There’s a channel for each crypto currency; bitcoin-feed, ether-feed and litecoin-feed. The act of sending data down a channel will create that channel if it doesn’t already exist. You don’t need to explicitly create a channel.
Tell the code what channels you want to receive data from by subscribing to them
Then listen for data and write some code to deal with the data when it arrives
I’ve written some HTML and CSS to make the prices look nice when they are displayed in the browser, but you can make it as simple or as fancy as you like.
What we are aiming for is to receive this data in Power BI so you don’t need to go nuts with your data presentation in the browser.
Get The Files
Both the publisher and subscriber files can be downloaded. These are HTML files and can be edited with a text editor – don’t use Word.
Enter your email address below to download the files.
By submitting your email address you agree that we can email you our Excel newsletter.
Please enter a valid email address.Getting the data Into Power BI
Now we have our publisher running, we can go back to Power BI and start receiving the data.
If you haven’t already got a workspace then create one so you can keep things neatly organised.
If everything is OK and the publisher code is running, Power BI will be able to connect to the channel and receive some data whch it will present like this.
If there’s a problem, Power BI won’t receive any data and it will give you an error. If that happens, check that you have entered the sub key and channel name correctly and that the publisher code is running in your browser.
Creating a Dashboard
With the streaming dataset created we can now use it in a dashboard.
I’ll use a Card visualization, and there’s only one field to display
You should now have a tile showing real time updates for the price of Bitcoin in USD.
Using PubNub is a lot easier than writing code to use the Power BI API to get real time data into your dashboards.
Even if you only have a little bit of knowledge of how to program it’s worth giving it a go to see what you can do.
Check with your data provider to see if they publish their data to PubNub.
Hadoop, Data Science, Statistics & othersData Science vs Business Intelligence: Head-to-Head Comparison (Infographics)
Here are the top 20 comparisons between Data Science vs Business Intelligence:Data Science vs Business Intelligence: Key Differences
Generic steps followed in Business Intelligence are as follows:
Set a business outcome to improve.
Decide which datasets are most relevant.
Clean and prepare the data.
Design KPIs, reports, and dashboards for better visualization.
Set a business outcome to improve or predict.
Gather all possible and relevant datasets.
Choose an appropriate algorithm to prepare a model.
Evaluate the model for accuracy.
Operationalize the model.Data Science vs Business Intelligence: Comparison Table
Basis Of Comparison Data Science Business Intelligence
Complexity Higher Simpler
Data Distributed and Real-time Siloed and Warehoused
Role Using statistics and mathematics to uncover hidden patterns, analyze data, and forecast future situations. Focused on organizing datasets, extracting useful information, and presenting it in visualizations such as dashboards.
Technology With cut-throat competition in today’s IT market, companies are striving for innovation and easier solutions for complex business problems. Hence, there is a greater focus on data science than business intelligence. BI is about answering complex business questions through dashboards, providing insights that may not be easily discovered through Excel. BI helps to identify relationships between various variables and time periods. It enables executives to make informed business decisions based on accurate data.
BI does not involve prediction.
Usage Data science helps companies to anticipate future situations, enabling them to mitigate risks and increase revenue. BI helps companies perform root cause analysis to understand the reasons behind a failure or to assess their current situation
Focus Data Science focuses on the future. BI focuses on the past and present.
Data science is the combination of three fields: Statistics, Machine Learning, and Programming.
Until now, most reporting tasks and BI tasks have been conducted through Excel.
Evolution Data science has evolved from Business intelligence. BI has been around for a long time, but previously it was mainly limited to Excel. However, now there are a plethora of tools available in the market that offer better capabilities.
Process Data science leans towards novel experimentation and is dynamic and iterative in nature. Business Intelligence is static in nature with little scope for experimentation. Extraction of data, slight munging of data, and finally dashboarding it.
Flexibility Data Science offers greater flexibility as data sources can be added as per future needs. BI has less flexibility. Data sources need to be pre-planned and adding new sources is a slow process
Business Value Data science brings out better business value than BI, as it focuses on the future scope of the business. Business intelligence has a static process of extracting business value by plotting charts and KPIs, thus showing less business value compared to data science.
Thought Process Data science helps generate questions, which encourages a company to run in a strategic and efficient manner. Business intelligence helps answer already existing questions.
Data Quality Data science involves analyzing data using statistical techniques and evaluating the accuracy, precision, recall value, and probabilities. It instills confidence in the decision-makers. BI provides high-quality dashboarding, but only with good quality data. This means that the data should be sufficient to extract insights from the dataset.
Method Analytic & Scientific Only Analytic
Questions What will happen?
What is happening?
Approach Proactive Reactive
Expertise Role Data scientist Business user
Data Size The tools and technologies are not enough to handle big datasets.
Use cases Not a periodic task. Many of the use cases of BI involve generating and refreshing the standardized dashboards.
Consumption Data science insights are consumed at various levels, from the enterprise level down to the executive level. Business intelligence insights are consumed at the enterprise or department level.Conclusion
Business intelligence is undoubtedly a beneficial starting point for any industry. However, in the long run, adding a layer of data science will ultimately set it apart. The ability to predict the future by analyzing data is an achievement of data science. Therefore, data science plays a pivotal role and is superior to business intelligence.Recommended Articles
Here are some further related articles to the subject:
Difference Between Data Engineer vs Software Engineer
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Data engineers design methods for storing, organizing, and retrieving software engineers’ data for their systems and applications. Data engineers have emerged as a distinct skill within the software engineering profession since they are trained to handle tasks not assigned to the software engineering department. APIs that are strong and well-documented and designed to get historical data from a third party are used by data engineers to obtain information. Research shows over level % of data engineers have previously worked as software engineers.
Data engineers are experts in the field of software development. They are in charge of data analysts people for to so they can make crucial decisions. A data engineer’s responsibilities include distributed computing, complex data structures, data pipeline development, and which is similar to other parallel programming languages. Data engineers must regularly refresh their skills in Kafka, Hadoop, Hive, Spark, and other software libraries. The best understanding of programming languages, databases, and tools can collect the query, store, and retrieve other data from databases to succeed as a data engineer. Data engineers have additional responsibilities related to the recent expansion of Big Data and preventing legal modifications and privacy concerns in the programming logic. Also, it will need the confidence to navigate new environments and good knowledge of databases and Java programming languages required of a data engineer.Head-to-Head Comparison Between Data Engineer vs Software Engineer (Infographics)
Below are the top 9 differences between Data Engineer vs Software Engineer:Key Difference Between Data Engineer vs Software Engineer
Let us discuss some of the major key differences between Data Engineer vs Software Engineer:
Software Engineer: Beyond code, software developers used and implemented the other technical logic implemented in the application. A software engineer may specialize in databases and other technical concepts, which depend on the firm’s needs. Although the duties appeal to you right away, concentrate on more skills in the engineering area. Several professional qualifications have n number of skills to recruit engineers in the firm. The variables may lead to a lucrative software engineering position.Comparison Table of Data Engineer vs Software Engineer
It must be an expert in the software development areas. The abilities of a software engineer are similar to those of a software developer.
A data engineer gets less salary when compared to a software engineer. A software engineer can make up to 40% more than a data engineer.
Data engineers, on the other hand, are more micro-focused. Software engineers take a more “macro” approach.
They must also concentrate on implementing the code that improves the efficiency of these systems. Data engineers are typically weaker programmers than software engineers.
If you’re a data engineer, you approach problem-solving differently than a software engineer. When compared to a data engineer, it needs problem-solving skills, but a little bit of difference is there.
If you’re a data engineer, you probably like to put more logic on the SQL side. This preference is based on your abilities. To map data from several providers, a data engineer must create categories. You must categorize so that name standards and mapping can be simplified. A software engineer collaborates with programmers, designers, and other professionals to create business-critical software applications and systems.
You have improved your SQL skills because you are constantly modeling, structuring, and manipulating data as a data engineer.
One of its sub-fields is data engineering. Under that umbrella, data engineers are specialists. Software engineering is a broad term that encompasses a variety of disciplines.
An engineer that works with data management systems is known as a data engineer. A software engineer’s responsibilities include OS development, software design, and back-end development, among other things.Conclusion
The list of particular roles you want your new team member to fill is the most important thing to consider when picking between a data engineer and a software engineer. In many circumstances, teams would benefit from having both a data engineer and a software engineer on board, as well as a variety of additional positions.Recommended Articles
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