Your typical data engineering<\/a> strategy now requires a variety of roles:<\/p>\n According to a recent report from Allied Market Research<\/a>, companies lack skilled engineers with a modern approach to data engineering. This makes it hard for businesses to leverage their data and build an effective data engineering roadmap. We\u2019ve seen this issue with many clients.<\/p>\n For example, Intellias recently helped a national telecom provider migrate to the cloud<\/a> to optimize its data infrastructure. The client had been handling hundreds of terabytes of data in a legacy system, leading to multiple inefficiencies and increased costs. Our qualified engineers helped the company reduce data processing time and CPU load, resulting in a more efficient system.<\/p>\n Read on to learn more about the required steps to build a data engineering strategy, the industry\u2019s best practices, and how our engineers can help.<\/p>\n Develop a comprehensive roadmap for collecting, storing, processing, and analyzing your business data with data strategy consulting by Intellias <\/p>\n Experts estimate<\/a> the global big data implementation and data engineering market will hit the $169.9 billion mark by 2029. That makes sense since to develop intelligent systems like high-frequency trading platforms, predictive analytics, and personalized recommendation engines, you need modern and efficient big data analytics systems.<\/p>\n Data engineering strategy isn\u2019t just about cutting-edge solutions for large enterprises, either. Midmarket businesses often consume vast amounts of data from external systems, field teams, IoT sensor arrays, user inputs, and more.<\/p>\n Source: ResearchGate<\/a><\/em><\/p>\n As companies grow, the number of data sources and data types they need to manage multiplies. It gets increasingly difficult to process all this incoming data without delays or data loss. To mitigate these issues, you\u2019ll need to come up with a detailed strategy for data engineering in big data.<\/p>\n Implementing modern data engineering principles in your strategy has many benefits:\u200b\ufffc\u200b<\/p>\n Big data engineers use their in-depth knowledge, understanding of distributed and scalable cloud systems, and various specialized tools to create a data implementation strategy. They build high-performance data pipelines that consolidate data, transform it according to predefined rules, and then send it to designated storage destinations. After that, the ball is in the court of data analysts and data scientists.<\/p>\n A big data engineer can use different technologies and tools depending on your business needs:<\/p>\n It\u2019s important to understand that tools alone don\u2019t get the job done. Ensuring an uninterrupted flow of data, its automatic conversion, and transformation requires a wide outlook on the business needs of the company and a thorough understanding of its infrastructure.<\/p>\n It also requires an ability to construct a flexible and scalable framework feeding perfectly structured, clean data outside. Additionally, it is typically assumed that data engineers are responsible for data security, integrity, and the overall support and maintenance of the pipeline.<\/p>\n All of the above, combined, makes the job of a data engineer a vital element of any company\u2019s big data engineering strategy. A recent LinkedIn job market report which placed Artificial Intelligence Engineers as 10th on the list of the most popular emerging jobs demonstrates the importance of big data engineering across industries.<\/p>\n The experts at Intellias have created dozens of strategies for data engineering solutions across various sectors. Based on that experience, here\u2019s how to build a data engineering strategy from scratch for your business:<\/p>\n Start building your data engineering strategy by identifying and understanding the challenges faced by your company. While your challenges may vary depending on your project, these three are incredibly common:<\/p>\n The experts at Intellias always begin their data engineering services by identifying challenges and conducting preliminary research. Properly scoping the project is one of the most important data engineering best practices to reduce extra costs and optimize all processes along the way.<\/p>\n Ask yourself the following questions:<\/p>\n Answering these questions will help you get a full understanding of how to implement a data engineering strategy in your company. Also consider your needs for backups, data quality audits, security assessments, and performance reviews.<\/p>\n Choose the best tools and frameworks depending on your pipelines\u2019 complexity and requirements:<\/p>\n The right technologies are essential to your big data strategy. They help you launch the digital transformation process faster and assess your needs during the early stages so you can make adjustments. Your data stack may also include large language models and data analytics tools<\/a>.<\/p>\n You\u2019ll have to develop methods to monitor data channels and capture incoming data. Consider these elements in your data operations:<\/p>\n Effective monitoring is essential for maintaining the integrity of your data pipeline. You can also use other tools depending on your expertise and needs, but these are some of the most popular choices. Convert and transform data to match the format and schema of the target destination. You\u2019ll have to use several data engineering techniques for this step:<\/p>\n Automation: Automate transformation processes with scripting languages like Python or Scala, applying frameworks such as Apache Airflow or Prefect to orchestrate and manage complex workflows. Proper transformation and conversion will help you integrate data seamlessly into your ETL pipeline. It will also ensure everything meets the required standards.<\/p>\n If you\u2019re serious about big data, you\u2019ll want to integrate DataOps practices into your approach. Check out our guide on the importance of DataOps<\/a> to learn what DataOps is and how it\u2019s revolutionizing big data in engineering.<\/p>\n Store the processed data in the target destination. You have a variety of options:<\/p>\n It\u2019s important to know the differences between a data warehouse and a data lake to understand how they work. Some key points to remember:<\/p>\n Intellias always suggests creating a system that is accessible, actionable, and visible across the organization. This helps avoid unnecessary data silos and ensures that everyone can find and use the data they need.<\/p>\n Change is inevitable, so you\u2019ll need to create mechanisms to handle changes in data schemas and business logic efficiently. Your data structures and their defined rules will need to adjust to new fields, types, names, and relationships. Be prepared to handle these changes with strategies such as:<\/p>\n This will help you get additional flexibility and maintain data accuracy. Also, automating schema changes minimizes downtime and ensures that data pipelines continue to function smoothly, even as underlying data structures evolve.<\/p>\n Regularly maintain and optimize your data pipelines for performance and reliability to ensure smooth and efficient operations. The best practices in data engineering depend on the following factors:<\/p>\n Timely maintenance and optimization are necessary to prevent bottlenecks and let your data flow without obstacles. This will help your company get all insights on time with no delays.<\/p>\n Your budget isn\u2019t infinite, so you\u2019ll have to manage costs effectively. Follow these points to minimize expenses and get the most value in return:<\/p>\n There are many ways to save your budget from extra costs. However, it takes technical experience and expertise to define these opportunities and maintain maximum value. For example, Intellias helped Germany\u2019s first fully digital bank set up a cost-efficient and effective data lake platform. Our platform development experts will help you get all the best solutions for your project.<\/p>\n \u200b\u200bYou\u2019ll need a reliable team of data engineers with expertise in your product\u2019s industry. After all, you\u2019re trusting them to follow all these steps and help your business build a reliable solution that brings valuable results. \u200b\u200b\u200b<\/p>\n The professionals at Intellias have 20+ years of experience in the market. Our expertise spans cloud-native architectures for rapid deployment and management of next-generation data infrastructures, ensuring operational efficiency and cost savings while minimizing errors through transparent, AI-driven decision-making processes.<\/p>\n Optimize your data flows to increase productivity, improve operational efficiency, and establish consistent data governance <\/p>\n Following the industry\u2019s data engineering best practices is key to creating high-quality data solutions in any company. We gathered the most valuable practices based on the experience of our engineers.<\/p>\n Modularity involves designing data systems as discrete modules, each addressing specific problems. Segregate datasets into modules based on their use or category to enhance data management. This approach simplifies code readability, reusability, and testing. Modular systems are easier to maintain, and make it easier for new team members to quickly understand and contribute to the project.<\/p>\n Source: GeeksForGeeks<\/a><\/em><\/p>\n The modern practice is to use AI to get rid of most routine tasks in data engineering. Automating data pipelines increases productivity and ensures consistency in data processing. Automated pipelines handle data extraction, transformation, and loading without manual intervention, saving time and reducing errors. Use tools like Apache Airflow or Prefect to set up reliable and efficient automated pipelines. Source: Estuary.dev<\/a><\/em><\/p>\n You can speed up data processing and improve development productivity by creating reusable solutions for common issues. Design data patterns that address repetitive issues efficiently instead of starting from scratch every time. First identify repeatable issues, then build standard processes to handle them effectively.<\/p>\n Source: Upsolver<\/a><\/em><\/p>\n Implement robust security policies to protect data from potential threats. This means tracking all data-related actions and setting rules for secure data access. Categorize data sensitivity issues and define solutions to mitigate risks, then create comprehensive documentation to codify data safety practices and guide new team members. If your organization ever undergoes a security or compliance audit, good security documentation will be critical to passing it.<\/p>\n Source: Venture in Security<\/a><\/em><\/p>\n Documentation isn\u2019t just for security. Keep detailed records of all aspects of data management, from sourcing to processing. Proper documentation helps everyone on the project understand the data pipelines and security policies inside and out. This practice prevents misunderstandings, ensures continuity, and facilitates onboarding for new team members.<\/p>\n DataOps is a collection of data practices designed to promote collaboration and efficiency in data analysis. It deals with the entire data lifecycle, from data gathering to successful analysis. DataOps combines different tools and methods to analyze data well. It is increasingly being adopted as a cloud data engineering best practice. K21Academy<\/em><\/a><\/p>\n Intellias provides DataOps services<\/a> that help companies achieve transparency and structure in their data flows. Your data analytics will take on a new life with our team\u2019s expertise.<\/p>\n Intellias is a global technology partner with expertise in designing data engineering strategies. We craft scalable end-to-end data processing solutions that give our clients the ability to extract meaningful insights from diverse data sources, regardless of size or complexity. We routinely help companies consolidate data silos and build future-ready platforms. These strategic data engineering solutions enable data-driven decision-making that accelerates market insights, enhances competitive advantage, and drives revenue growth.<\/p>\n Explore data engineering examples from a few of our customer case studies:<\/p>\n Data strategy guidance for a global construction brand.<\/strong><\/p>\n Digital retail consulting to orchestrate data flows and operations.<\/strong><\/a><\/p>\n\n
Importance of a Data Engineering Strategy<\/h2>\n
![\"high-frequency](\"https:\/\/d17ocfn2f5o4rl.cloudfront.net\/wp-content\/uploads\/2020\/10\/Data-Engineering-Strategy-Infographic.jpg\")
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![\"Data](\"https:\/\/d17ocfn2f5o4rl.cloudfront.net\/wp-content\/uploads\/2020\/10\/pic-2.jpg\")
<\/p>\n10 steps to implement a data engineering strategy<\/h2>\n
1. Identify Challenges<\/h3>\n
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2. Choose the right tools<\/h3>\n
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3. Monitor data channels<\/h3>\n
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<\/p>\n4. Transform and convert data<\/h3>\n
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5. Save to target destinations<\/h3>\n
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6. Handle schema changes<\/h3>\n
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7. Maintain and optimize<\/h3>\n
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9. Balance costs and resources<\/h3>\n
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10. Partner with professionals<\/h3>\n
Best practices of big data engineering<\/h2>\n
1. Modular approach<\/h3>\n
![\"Modular](\"https:\/\/d17ocfn2f5o4rl.cloudfront.net\/wp-content\/uploads\/2020\/10\/Data-Engineering-Strategy-Infographic-1.jpg\")
<\/p>\n2. Pipeline automation<\/h3>\n
\n![\"Automating](\"https:\/\/d17ocfn2f5o4rl.cloudfront.net\/wp-content\/uploads\/2020\/10\/Data-Engineering-Strategy-Infographic-2.jpg\")
<\/p>\n3. Maintain repeatability<\/h3>\n
![\"Design](\"https:\/\/d17ocfn2f5o4rl.cloudfront.net\/wp-content\/uploads\/2020\/10\/Data-Engineering-Strategy-Infographic-3.jpg\")
<\/p>\n4. Security policy for database management<\/h3>\n
![\"security](\"https:\/\/d17ocfn2f5o4rl.cloudfront.net\/wp-content\/uploads\/2020\/10\/Data-Engineering-Strategy-Infographic-4.jpg\")
<\/p>\n5. Maintain proper documentation<\/h3>\n
6. Apply DataOps<\/h3>\n
\n![\"Apply](\"https:\/\/d17ocfn2f5o4rl.cloudfront.net\/wp-content\/uploads\/2020\/10\/Data-Engineering-Strategy-Infographic-5.jpg\")
<\/p>\nThe Intellias experience<\/h2>\n
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