What are some common problems data engineers solve?

Data integration is not a single-step process and often involves a complex sequence of activities. In my experience, key challenges are: ?? Handling different data formats / structures from disparate sources. ?? Ensuring data quality by handling issues like missing values, duplicates, inconsistencies, and inaccuracies demands comprehensive data profiling, cleansing, and validation strategies. ?? Maintaining synchronisation and consistency across various systems while handling constantly changing data requires sophisticated solutions. ?? Data volume will keep growing. Techniques like parallel processing, indexing, and caching helps in enhancing integration performance.

As a Data Engineer one needs to solve many issues while working on data integration tools such as Airflow, AWS Glue etc. 1. Airflow supports different providers like Databricks, Snowflake, Azure to connect with them but Airflow version mismatch can give you compatibility issues as well. 2. Such tools doesn't provide some inbuilt mechanism to check the data ingestion JSON data. We need to write the code to validate that as well. 3. Resolving any issue with Apache Spark needs a lot of expertise. Such problems come while working.

Data Ingestion: Problem: How to analyze insights from diverse data sources? Solution: Data engineers bring data together with tools like Apache Kafka or Apache Nifi, enabling efficient and reliable data ingestion for insights. Data Cleaning: Problem: How to get clean data from various sources and types? Solution: Data engineers use tools such as Apache Spark for better accuracy and consistency in data cleaning and transformation. Data Warehousing: Problem: How to store large data for quick retrieval? Solution: Data engineers use tools like Amazon Redshift or Google BigQuery to build and maintain data warehouses for efficient data storage and retrieval.

Data integraetion from multiple sources, data storage and retrieval opmization, ensuring data quality and consistency, building scalable data pipelines, managing big data processing, and implementing data security and privacy measures.

In my experiences; some common points to solve are: ??Automatication of data mapping. ??Build a framework to build data pipeline. ??Control of connection with data sources for extraction processes. ?? Reprocessing data to generate right value.

From my experience, keeping data model flexible is essential. Key challenges in data modeling involves ?? Translating human requirements into data models. Understanding business current and future need is essential ?? Data could be structured, semi-structured, unstructured. Designing models that accommodate these different data types effectively. ?? Optimising for query performance. Making decisions around indexing, partitioning, normalisation vs denormalisation

Data Model needs to be flexible. If it is a unified single source of truth type of Data Model across the different data products we are building, then it is truly scalable. By flexibility it means there could be a combination of OBT and Kimball that could be used depending on the use cases.

Employing various modeling techniques, such as ER diagrams, dimensional modeling, is essential to represent data structures effectively. Utilizing tools like ERwin, Hackolade, they translate conceptual models into logical designs, ensuring data integrity and efficient storage. Iterative refinement is common, and engineers collaborate closely with stakeholders to validate and adjust models. Additionally, they optimize for performance by considering indexing, partitioning, and normalization. In the big data landscape the DB type and file format plays a crucial role. The goal is to create a robust foundation for data storage and retrieval, facilitating accurate analytics & supporting the organization's data-driven decision-making processes

In my experiences; some common problems related data modeling are: ?? Update column in rows that ingested with wrong value. ?? Add new column in tables and fill this column. ?? If store in data lake; handle and repair ID field for all rows inside of table.

My perspective about data quality that refers to the accuracy, completeness, consistency, reliability, and timeliness of data. - It is a measure of how well data meets the requirements and expectations of its intended use. High data quality is essential for making informed decisions, conducting meaningful analyses, and ensuring the reliability of business processes. - Conversely, poor data quality can lead to errors, misinformation, and decision-making based on inaccurate or incomplete information.

Strategies that have worked for us in keeping data quality high: ?? Validation processes in real-time to ensure incoming data adheres to predefined quality standards before being stored or processed. ?? Adding domain-specific rules to ensure data conforms to the business requirements and standards. ?? Statistical analysis to identify anomalies, trends, or patterns in the data

From my perspective, it is essential to initiate the focus on data quality right from the requirement phase, aligning it closely with the needs of our business stakeholders. In addition to adhering to fundamental Data Quality standards such as completeness, consistency, accuracy, and others, the timeliness of data becomes a crucial parameter. By delivering the right data at the right time to downstream systems, we can significantly enhance the quality of the reports and dashboards developed. Moreover, treating data quality checks as a continuous process is important. Implementing alert and notification mechanisms on the data can help identify any deviations from the established data quality parameters.

Data engineers employ data profiling techniques to assess the integrity of datasets, identifying anomalies and inconsistencies. Utilizing automated cleansing processes, they rectify inaccuracies and standardize formats, ensuring uniformity across the data. Additionally, data engineers implement validation rules to detect errors in real-time, preventing flawed entries from compromising the overall quality. Regular monitoring and maintenance routines are established to uphold data integrity over time. Through collaboration with data scientists and domain experts, data engineers continuously refine processes, establishing a robust framework for maintaining high-quality data throughout its lifecycle.

In my experiences; some common problems to solve in data quality field are: ?? Resolve doubt requests in definition of threshold in metrics. ?? Guide all change requests definition of rules: validate who is authorized to give me a new definition to apply. ?? Handle custom alerts and reports for differences teams based in same group of metrics and rules.

Data engineers design & maintain secure data pipelines, employing encryption techniques to safeguard sensitive information during transit & at rest Implementing access controls & authentication mechanisms, they ensure that only authorized personnel can access critical data Regularly auditing & monitoring data infrastructure, they swiftly identify, respond to potential security breaches They collaborate with cybersecurity experts to stay abreast of evolving threats, proactively enhancing security measures to fortify data integrity & protect against unauthorized access or data breaches.

Enough adversarial testing is important when releasing data-products, especially chatbots, copilots or any NLP interfaces. With this, prompt injections and further techniques should be used.

I dare to predict a new role blending data engineering and cybersecurity—a cyber-data engineer whose role will become increasingly relevant and in demand as data becomes more critical and ... expensive. The need to secure it is increasing and extending to data at rest and in transit.

In my opinion, data optimization is a foundational aspect that contributes to the overall effectiveness of data-driven applications and analytics. The future of data optimization involves a shift towards intelligent, adaptive, and decentralized systems. Machine learning, AI, and advancements in edge computing will shape a landscape where data processing is not only optimized for speed and efficiency but also aligns with sustainability and emerging technological paradigms.

A otimiza??o de dados é fundamental para se retirar uma análise mais precisa sa massa de dados coletada, ela gerar uma.interpreta??o melhor e mais limpa para o objetivo que se dá.

In my opinion, the data?? innovation isn't just about using data to solve problems we already know about. It's also about exploring new areas, finding opportunities we didn't know existed, and trying out new things with data. It's like an adventure where we use information in creative ways to discover new possibilities and make things better. It requires a combination of technical expertise, creativity, and a willingness to experiment with data in ways that can transform how organizations operate and derive value from their data assets.

In my experience, exploration of raw data as soon as it becomes available provides key inputs and value to how to go about a problem or may be even identify something entirely different or new. Keeping yourself open to data exploration at all times is vital

Data engineers should consider not being married to a particular tool or technology. It’s going to keep changing. Finding the best solution or technology for the most typical business problems to be solved should be the correct approach.

In simple words data Engineers are responsible to provide data to business for further analysis. So making available the quality data to right group at right time is the key job of any data Engineer. To achieve above, making a scalable and robust solution is recommend. The data flow solution which have adequate provision for data quality and data governance is go to job of Data Engineer.

?? I will give a more point clearer and contextual: this question is sooo wide because depend of stage; designing, documentation, implementation, assesoring, consulting; that data team face based in organization maturity; then many common problems can be resolved and new few ones appear in next madurity level since all common problem can be handled easier.