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5 Painful Mistakes Data Engineers Make and How to Avoid Them

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It is always called that data science is the sexiest job in the 21st century. But now, data engineering careers are poised to give data scientist’s tough competition. Data engineering jobs are gaining more attention than data science jobs. When you decide that data engineering is your field, you need to understand that becoming a data engineer is a career, not a destination. Usually, people talk about the positive aspect, but nobody talks about the mistakes and how to avoid them.

So, it’s not easy, and data engineering professionals keep complaining that there is a massive gap between self–educated data engineer skills and real–world working in that field. Here are five common mistakes and traps that even the most skilled data engineers can fall into and what you can do to avoid pitfalls.

Common Mistakes that Data Engineers Make

The following are the common mistakes usually made by data engineers in their careers.

1. Not Considering Needs of End Users

One of the most common mistakes that data engineers usually make is paying attention to the needs of the end users. Many articles published on the internet discuss this topic, but engineers still avoid it. The data engineering decision is driven by the organization’s goal and customer needs.

So, it is best to ask questions:

· What tools and programs can you access?

· Are your data structures easy to access?

· What are the general skillsets?

· Is the end user known about SQL?

Even the most technologically advanced system is good as its end-user usefulness, so their requirement should be prioritized in the development process.

2. Builds Complex Logic in All

Data becomes complex, and delivery timelines shrink, so it is the nature of data engineers to build complex systems. Complex systems have thousands of lines of code, and only some are structured, making them difficult to maintain. Often, it becomes impossible to debug when an issue occurs and is only solved by the original developer. Thus, it is the responsibility of data engineers to build a simple system that is easy to understand for a newbie.

It is crucial to maintain an excellent modular structure for work, build functions that are easy to understand, and use appropriate naming conventions. Also, remember that you don’t have to plan everything when designing scalable architectures. What may seem obvious now might change later. You should have the plan to scale your software and how you will respond to evolving needs without causing any inconveniences for current users.

3. Not Asking Questions

The most significant mistake data engineers make in their starting careers is not asking questions. The data is carved in stone, but without clarity of how it should be put together and its purpose, new data engineers often lose sight of what they are trying to accomplish. Discovering the desired business benefit of each project makes prioritization on how to scrape and manage data smoothly.

So, raising the question in any project is essential, and they are more crucial than ever in the world of data. Asking questions will enable you to avoid costly mistakes, miscommunications, and misunderstandings among team members. At every step of the project, you need to ask questions because it ensures what is building and what is required.

A few of the popular questions are:

· What data needs to be collected?

· What problems are we trying to solve?

· What is the deadline for completing the task?

· What do we need to solve the problem?

· What is the time limit to complete the project?

4. Not Making their Fundamentals Strong

Most data engineers make a significant mistake by not making their fundamentals strong. It is expected from data engineers to have strong command in coding/scripting and SQL. However, if a data engineer does not work on a simple project and directly jumps to write a complex data pipeline, it creates a mess in coding.

So, data engineers should be conversant enough in databases and relational database management systems. It will create problems even in the simple data model if you don’t understand the difference between the primary and surrogate keys, it will create problems even in the simple data model.

5. Not Checking Data Accuracy

Many types of data are used in the systems you designed. These data may come from many sources. Data engineers must be aware of the importance of accuracy as there are

more information sources daily. Let’s assume you are the data engineer responsible for your company’s sales and marketing systems. You might be responsible for building data pipelines or dealing with different types of information.

· Social media data.

· Search engine data.

· Data warehouses and ERPs can provide order information data.

· Salespeople and employee information from the HRMS systems.

· Information and financial forecasts.

The social media data and search engine data are intuitively interpreted. However, it requires a lot more cleaning. You might think upstream systems such as ERPs or data warehouses would have clean data. Despite your best efforts to ensure accuracy, errors and issues can always occur in intermediate transformations before you receive the data. This could have an enormous impact on the system you create.

Data engineers should remember the golden rule. Only assume that the data is correct if you’ve done your checks. To ensure accuracy, you should include standard checks in your development process. SQL allows you to create queries that highlight discrepancies at your end. This can make the difference between success and failure in a project.

Conclusion

Data engineering is a great way to build a career in the data world. The demand for data is increasing, and the development of big data technologies to aid in response to the demand. Job opportunities are growing, and many people are attracted to them, ultimately increasing the competition. So, obtaining data engineering certification and differentiating you from others is essential.

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Which is better for Real Estate: SEO or PPC Ads?

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Real estate agents and brokers are always looking for the most effective digital marketing agencies to attract potential home buyers and sellers. Two of the most common options utilized are search engine optimization (SEO) and pay-per-click (PPC) advertising. However, the question remains – which is a better investment for real estate companies, SEO or PPC ads? In this article, we will explore the key differences between SEO and PPC and how each can benefit real estate marketing efforts.

SEO vs PPC: The Basics

Let’s start with defining SEO and PPC. SEO refers to the process of optimizing web pages and content to rank higher in organic (non-paid) search engine results pages (SERPs). The goal is to attract free traffic from search engines like Google through relevant, high-quality content and technical optimizations. PPC advertising, on the other hand, focuses on paying to display ads in search results or on other sites through ad networks like Google Ads. With PPC, advertisers only pay when a user clicks on their ad.

SEO is a long-term strategy that takes time to see results, as search engines are constantly changing their algorithms. PPC provides more immediate access to traffic but requires an ongoing advertising budget. SEO helps build brand awareness and authority over time through free exposure, while PPC focuses only on clicks and conversions without building brand equity.

Benefits of SEO for Real Estate

For real estate companies and agents, SEO offers several advantages compared to PPC:

  • Lower Costs – SEO is a one-time cost of optimizing content while PPC has continuous advertising fees. SEO saves money in the long run.
  • Targeted Traffic – SEO targets specific geographic areas and property types more effectively since search habits vary locally. PPC reaches broader audiences.
  • Trust and Authority – High organic rankings signal credibility and build trust with customers over time versus paid ads. This is important in real estate.
  • Extended Exposure – SEO content remains live indefinitely versus temporary PPC campaign durations, providing ongoing leads.
  • Synergistic Content – SEO content like guides, and neighborhood pages strengthen branding while PPC is generally just click-based ads.
  • Data and Insights – SEO affords to analyze search terms and page analytics to optimize. PPC provides less proprietary data and insights.

Benefits of PPC for Real Estate

However, PPC also has its advantages for certain real estate marketing goals:

  • Immediate Traffic – PPC delivers traffic right away without the months-long SEO ramp-up period. Good for time-sensitive listings.
  • Targeted Audiences – PPC allows ultra-targeting demographics, property interests, locations, etc. SEO targeting is broader.
  • Tracking and Optimization – PPC provides granular tracking of campaign, ad, keyword, and device performance for constant A/B testing and optimization.
  • New Listings and Inventory – PPC is ideal for promoting fresh listings and keeping inventory top-of-mind versus the patience required with SEO.
  • Lead Volume – When budget allows, PPC often outperforms SEO initially in raw lead generation and appointments booked.
  • Seasonal Campaigns – PPC facilitates focusing resources around busy seasons like spring/summer versus set-it-and-forget-it SEO.

So in summary, SEO is well-suited for sustained brand awareness, market position, and steady organic leads. PPC delivers prompt traffic but ongoing investment is required. A balanced approach using both is often most effective for real estate goals.

FAQs

Q: Which will get me more leads as a real estate agent – SEO or PPC?

A: In the long run, a successful SEO strategy will generate a larger volume of high-quality leads. But PPC can outperform SEO initially in raw lead numbers if the budget is high enough. Both should be used together for maximum lead generation.

Q: How long does it take to see results from SEO?

A: Most SEO experts estimate it takes a minimum of 3-6 months to begin seeing some results from SEO efforts, as Google’s algorithms are complex. Significant improvements may take 9-12 months. Results also depend on industry, competition level, and optimization thoroughness.

Q: Is SEO or PPC better for selling new listings?

A: PPC is generally a faster route to promoting new listings and inventory due to its immediate traffic. However, SEO content-supporting listings can still help over the long haul. An ideal strategy uses PPC initially and then transitions to SEO as the listing seasons.

Conclusion

In conclusion, both SEO and PPC advertising have clear benefits for real estate companies, but which is “better” depends entirely on marketing goals and budget. A truly optimized strategy leverages the strengths of both – using SEO for ongoing brand equity and organic lead generation supplemented by tactical PPC campaigns for short-term inventory goals, seasonal periods, and new developments requiring prompt attention. The most successful real estate marketing programs incorporate SEO and PPC advertising together rather than considering them an either/or choice. With data-driven optimization, real estate professionals can maximize traffic and leads from digital channels.

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Choose The Best Amazon Repricing Strategies

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Alpha Repricer, a cutting-edge solution designed specifically for Amazon merchants, allows you to take advantage of the power of dynamic pricing. Their technology employs advanced Amazon repricing strategies that adjust to market movements every 2 minutes to maximise your profitability. Stay ahead of the competition by easily customizing repricing rules depending on your own company requirements. Alpha Repricer enables you to intentionally beat or match competitor prices, ensuring that your products are always competitively priced. Setting up your repricing plan is simple with the user-friendly interface of Alpha Repricer. Sign up in under 60 seconds, with no upfront billing information necessary. Alpha Repricer’s advanced repricing solutions can help you grow your Amazon business.

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