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
Importance of a Data Engineering Strategy<\/h2>\n
Experts estimate<\/a> the global big data implementation and data engineering market to hit the $169.9 billion mark by 2029. The development of high-frequency trading platforms, predictive analytics, personalized recommendation engines, and many other intelligent systems requires the implementation of modern and efficient big data analytics systems.<\/p>\n It\u2019s not just about cutting-edge solutions for large enterprises. Even midmarket businesses may be consuming vast amounts of data from external systems, field teams, sensor arrays, users, and more.<\/p>\n Source: ResearchGate<\/a><\/em><\/p>\n Companies grow and the number of sources and data types multiply. Processing these streams without delays and data loss becomes a great challenge. Thus, mitigating these issues requires you to come up with a detailed strategy for data engineering in big data.<\/p>\n Implementing modern data engineering principles in your strategy helps with:<\/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. That\u2019s when the ball is in the court of analysts and 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. This is demonstrated by a recent LinkedIn job market report, which placed Data Engineers as 8th on the list of the most popular emerging jobs.<\/p>\n The experts at Intellias have created dozens of strategies for data engineering solutions across various sectors. Let\u2019s check out 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. These can include different options depending on your project:<\/p>\n The experts at Intellias always begin their data engineering services<\/a> by identifying challenges and conducting preliminary research. This is one of the data engineering best practices to reduce extra costs and optimize all processes.<\/p>\n You can also ask yourself the following questions to accelerate the transition to strategy execution:<\/p>\n Answering these questions will help you get a full understanding of how to implement a data engineering strategy in your company. You can also consider additional issues like backups, reviews, and anything else that can help during this process.<\/p>\n Choose the best tools and frameworks depending on your pipeline\u2019s complexity and requirements:<\/p>\n The right technologies are essential in your big data strategy. They help you launch the digital transformation process faster and understand all your needs during the early stages. This may also include the usage of large language models and data analytics<\/a>.<\/p>\n You\u2019ll have to develop methods to monitor data channels and capture incoming data. This requires you to consider several 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 Proper transformation and conversion will help you integrate data seamlessly into your ETL pipeline. It will also ensure everything meets the required standards. You might want to integrate DataOps practices into your approach. We covered the importance of DataOps<\/a> and its definition in our previous article. Check it out for a full understanding of big data in engineering.<\/p>\n Store the processed data in the target database, data warehouse, or data lake using efficient and reliable methods. Here\u2019s what you can use with these options:<\/p>\n You must also discern the differences between a data warehouse and a data lake to understand how they work. Some key points to remember:<\/p>\n Data warehouse<\/strong>: a centralized repository for structured data used for reporting and analysis.<\/p>\n Data lake<\/strong>: a place to keep unstructured, raw data in scalable cloud storage.<\/p>\n Some companies also used data silos. We generally recommend avoiding data silos because they are difficult to integrate with all your other data. Intellias always suggests creating a centralized system that is accessible, actionable, and visible.<\/p>\n For example, our approach in retail data engineering<\/a> with big data helped a company save millions of dollars in spoiled food stocks and reduce energy consumption by 20%.<\/p>\n Create mechanisms to handle changes in data schemas and business logic efficiently. Your data structures and their defined rules can change over time with new fields, types, names, and relationships. That\u2019s why it\u2019s necessary to handle these changes with such strategies:<\/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 pipeline for performance and reliability to ensure smooth and efficient operations. The best practices in data engineering require you to consider the following:<\/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 eternal, so you\u2019ll have to manage all the costs effectively. Follow these points to minimize expenses and get the most value in return:<\/p>\n There are many places to save your budget from extra costs. However, it takes experience and expertise to define these spots and maintain maximum value. For example, Intellias helped Germany\u2019s first fully digital bank<\/a> 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 You\u2019ll need a reliable team of data engineers with expertise in your product\u2019s industry. This will ensure they follow all these steps and help your business get a reliable solution that brings valuable results. Intellias has been in the market for 20+ years, and 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. This approach simplifies code readability, reusability, and testing. Modular systems are easier to maintain and allow new team members to quickly understand and contribute to the project. Segregate datasets into modules based on their use or category to enhance data management.<\/p>\n Source: GeeksForGeeks<\/a><\/em><\/p>\n 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 Luigi to set up reliable and efficient automated pipelines. The modern practice is to use AI to get rid of most routine tasks in data engineering.<\/p>\n Source: Estuary.dev<\/a><\/em><\/p>\n Design data patterns that address repetitive issues efficiently. You can speed up data processing and improve development productivity by creating reusable solutions for common issues. Identify repeatable issues and 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. Track all data-related actions and set rules for secure data access. Categorize data sensitivity issues and define solutions to mitigate risks. Create comprehensive documentation to ensure data safety and guide new team members.<\/p>\n Source: Venture in Security<\/a><\/em><\/p>\n 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 out. This practice ensures continuity and facilitates smooth transitions 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 used a lot in Azure data engineering best practices.<\/p>\n K21Academy<\/em><\/a><\/p>\n Intellias provides DataOps services<\/a> that help companies get transparency and structure in their data flows. You\u2019ll get a new meaning in your data analytics with our team\u2019s expertise.<\/p>\n With expertise in designing data engineering strategies, Intellias excels in crafting scalable end-to-end data processing solutions that extract meaningful insights from diverse data sources, regardless of size or complexity. By consolidating data silos and building future-ready platforms, we enable data-driven decision-making that accelerates market insights, enhances competitive advantage, and drives revenue growth.<\/p>\n Here are our leading data engineering examples and case studies:<\/p>\n Data strategy guidance for a global construction brand.<\/strong><\/a><\/p>\n Digital retail consulting to orchestrate data flows and operations.<\/strong><\/a><\/p>\n A platform for equipment monitoring in supply chains.<\/strong><\/a><\/p>\n![\"high-frequency](\"https:\/\/d17ocfn2f5o4rl.cloudfront.net\/wp-content\/uploads\/2020\/10\/Data-Engineering-Strategy-Infographic.jpg\")
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<\/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
![\"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
![\"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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