As the volume of data continues to grow, so does the need for efficient data management. This is where data engineering comes in - it involves designing, building, and maintaining the infrastructure that is used to collect, store, and process data.
Without proper data engineering, businesses risk losing valuable insights and potential revenue.
Delivering trusted data pipelines at scale
The Importance of Data Engineering
Big data brings together more data, from more sources in order to to provide companies with better insights into their business. Cloud adoption adds new complexity and yet more data sources.
Table of Contents
- Data Engineering for Scale
- Components of Data Engineering
- Understanding data lineage and impact in Data Engineering
- Delivering Trusted Data
- Ensuring data privacy
- FAQ about Data Engineering
As the volume of data continues to grow, so does the need for efficient data management. This is where data engineering comes in - it involves designing, building, and maintaining the infrastructure that is used to collect, store, and process data. Without proper data engineering, businesses risk losing valuable insights and potential revenue.
Companies that make effective use of Big Data and Analytic increase their productivity and profitability by 5 to 6 percent over those that don't.
Yet, for most companies, big data analytics remains a bridge too far. Obstacles such as the technical complexity of new "big data" architectures, combined with the high cost and scarcity of skilled staff, stop many big data initiatives in their tracks.
Big data brings together more data, from more sources in order to to provide companies with better insights into their business.
Core to big data is the principle of data discovery - identifying previously unknown patterns of behaviour about our customers, our markets, our offerings and our operations that allow us to radically improve the way we do business.
Big data is not just another BI tool - it is a whole new way to deliver insight
Companies that are able to make effective use of big data and analytics increase their productivity and profitability by 5 to 6 per cent over those that don't
Data Engineering for Scale
To ensure that data can be processed efficiently and at scale, data engineers must design and build data pipelines. A data pipeline is a series of processes that move data from one point to another, transforming it along the way. These pipelines must be designed to handle massive amounts of data and ensure that the data is processed accurately and reliably.
Data engineers must also consider the various sources of data that are available and determine how to integrate them into the pipeline. They must ensure that the data is properly formatted, cleaned, and validated before it is processed further. This is critical to ensuring that the data is accurate and reliable.
Companies are looking to gain better insights, more quickly, by drawing in more and more data from a variety of sources - including traditional databases, legacy systems (like mainframes), and, increasingly unstructured data sources such as the Internet of Things(IoT) and Blockchain
This complexity and volume can become overwhelming
Many data scientists spend more time trying to find and access data than delivering insights. In recent years, we have seen the emerging new role of the data engineer - solely responsible for the delivery of quality data for the use of the data scientist and other knowledge workers
It is important that these data engineers document their work - providing curated and documented data sets in a data catalogue that can be easily searched and supports data-sharing agreements.
Companies must also consider the use of self-service data preparation tools to allow more users to build robust data pipelines.
Components of Data Engineering
Big Data Preparation
ETL (extract, transform and load) has for years been a workhorse technology for enabling the analysis of business information. But now it’s being joined by a new approach, called data preparation or data wrangling. The two techniques are similar in purpose but distinct in function and application.
Where ETL is intended for IT professionals and works mainly with structured data sets, data preparation engines for big data must handle data of any type - both structured and unstructured - and is becoming more and more a self-service capability enabling both business and IT professionals to find and access the data they need to deliver trusted insights.
Precisely Connect seamlessly connects legacy data into next-generation cloud and data platforms with high-performance and low-maintenance ETL and CDC capabilities.
Precisely Data360 Analyze is a drag-and-drop data preparation and analytics tool that simplifies the process of building data pipelines to rapidly achieve business insights.
Understanding data lineage and impact in Data Engineering
Understanding data lineage and impact refers to the ability to trace the origin, transformation, and flow of data throughout its lifecycle, as well as comprehending the potential consequences and dependencies associated with data changes. It involves gaining insights into how data is sourced, processed, transformed, and consumed within an organization's data infrastructure and is a key element for DataOps.
Here's a closer look at these concepts:
Data Lineage: Data lineage provides a historical record of the movement and transformation of data from its source to its final destination. It tracks the data's journey through various stages, such as extraction, cleansing, integration, transformation, and loading. Data lineage helps answer questions like: Where did the data originate? How has it been modified? Which processes and systems have interacted with it? Understanding data lineage is crucial for ensuring data quality, compliance, and troubleshooting data-related issues.
Source Systems and Data Acquisition: Data lineage includes identifying the source systems or external sources from which data is acquired. It involves understanding how data is obtained, whether from databases, APIs, files, or other sources. This knowledge enables data engineers to establish data acquisition processes, data extraction methods, and data integration strategies.
Data Transformations: Data lineage also involves comprehending the transformations applied to the data during its journey. It includes understanding the data cleansing, filtering, aggregating, enriching, and formatting processes. By understanding data transformations, data engineers can ensure the accuracy, consistency, and integrity of data as it undergoes various changes.
Data Storage and Processing: Understanding data lineage requires knowledge of how data is stored and processed within the data infrastructure. It involves identifying the databases, data warehouses, data lakes, or other storage systems where data resides. Additionally, it encompasses understanding the data processing frameworks, technologies, and tools used to manipulate and analyze the data, such as Apache Spark, Hadoop, or cloud-based services.
Data Consumption and Impact: Data lineage helps track how data is consumed and utilized by different applications, reports, or downstream processes. It includes identifying the data consumers, such as business intelligence tools, analytics platforms, or machine learning models. Understanding data impact involves assessing how changes to the data can affect downstream processes, reports, and analytics, ensuring that data changes are properly managed and communicated to relevant stakeholders.
By understanding data lineage and impact, data engineers can effectively manage data pipelines, identify potential bottlenecks or issues, troubleshoot data inconsistencies, and ensure data integrity throughout the data lifecycle. It also helps in meeting regulatory requirements, maintaining data governance, and supporting data-driven decision-making processes.
In this interactive webinar recording, Neil Burton (CTO at Clean Data) and Jan Ulrych (VP of Research and Education at MANTA) ask the audience about their struggles and tell them how Clean Data together with MANTA addresses their issues.
Delivering Trusted Data
Data engineers must also ensure that the data that is processed is trusted. This means that the data is accurate, complete, and consistent. To achieve this, data engineers must isnipmplement various quality checks along the pipeline, including data profiling, data validation, and data cleansing. These checks ensure that the data is of high quality and can be trusted to provide accurate insights.
Your first thought might be analytics accuracy or the amount of data you have available to process. But if you’re not thinking about big data quality as well, you may be undercutting the effectiveness of your entire big data operation.
Consider the following ways in which data quality can make or break the accuracy, speed and depth of your big data operations:
- Real-time data analytics are no good if they are based on flawed data. No amount of speed can make up for inaccuracies or inconsistencies.
- Even if your data analytics results are accurate, data quality issues can undercut analytics speed in other ways. For example, formatting problems can make data more time-consuming to process.
- Redundant or missing information within datasets can lead to false results. For example, redundant information means that certain data points appear to be more prominent within a data set than they actually should be, which results in misinterpretation of data.
- Inconsistent data – meaning data whose format varies, or that is complete in some cases but not in others – makes data sets difficult to interpret in a deep way. You might be able to gather basic insights based on inconsistent data, but deep, meaningful information requires complete datasets.
We help you to leverage big data technologies to cleanse and match data at extreme scale.
Ensuring data privacy
Data engineers must also ensure that the data that is processed is secure.
Dynamic Access Management
Stop wasting valuable time engineering and provisioning one-off data sets for narrow use cases.
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FAQ about Data Engineering
What skills are required to be a data engineer?
Data engineers require a strong foundation in computer science, programming, and database management. They must also have an understanding of data modelling and be proficient in data-oriented languages such as SQL, Python, and Java. They may also require exposure to enterprise ETL, data preparation and data quality tools, amongst others.
What tools are used in data engineering?
Data engineers use a variety of tools and technologies, including databases such as MySQL, Oracle, and MongoDB, data processing frameworks such as Apache Spark and Apache Hadoop, data integration tools, and data lineage tools.
What is the difference between ETL and CDC?
ETL (Extract, Transform, Load) and Change Data Capture (CDC) are both data integration methods used to move data from one system to another. However, they differ in their approach and purpose.
ETL is a process that involves extracting data from various sources, transforming it into a format that is compatible with the target system, and loading it into the destination system. ETL is commonly used in data warehousing and business intelligence applications, where data from multiple sources is consolidated into a single system for analysis.
CDC, on the other hand, is a process that captures changes made to data in real-time or near real-time, and then propagates those changes to the target system. CDC is commonly used in scenarios where data needs to be synchronized between systems or where real-time analysis is required.
The main difference between ETL and CDC is in the way they handle data. ETL is a batch-oriented process that moves large amounts of data in scheduled intervals, while CDC is a continuous process that moves only the changes made to the data in real-time. ETL is best suited for scenarios where data needs to be consolidated and transformed, while CDC is best suited for scenarios where data needs to be synchronized in real-time.
In summary, ETL and CDC are both valuable data integration methods that serve different purposes. ETL is best suited for consolidating data from multiple sources into a single system, while CDC is best suited for real-time data synchronization and analysis.
What is the difference between data preparation and data integration?
Data preparation and data integration are both important steps in the process of data management. However, they have different purposes and objectives.
Data preparation involves the process of cleaning, transforming, and organizing data to make it suitable for analysis. This includes tasks such as removing duplicate or irrelevant data, correcting errors and inconsistencies, and formatting the data in a way that is appropriate for the analysis that will be performed. The goal of data preparation is to ensure that the data is accurate, complete, and consistent so that it can be used effectively for analysis.
On the other hand, data integration involves the process of combining data from multiple sources into a single, unified view. This can involve merging data from different databases or systems, or combining data from external sources such as social media or third-party vendors. The goal of data integration is to create a comprehensive view of the data that is accessible and usable for analysis, reporting, and other business purposes.
In summary, while data preparation involves cleaning and organizing data to make it suitable for analysis, data integration involves combining data from different sources to create a unified view. Both steps are important in the process of data management and are often performed in conjunction with each other.
How does DataOps help data engineering?
DataOps, which stands for Data Operations, is a methodology that combines the principles of Agile development, DevOps, and Lean manufacturing to improve the efficiency and effectiveness of data-related operations. DataOps can be helpful for data engineers in a number of ways:
Faster time to market: DataOps promotes a collaborative and iterative approach to data development, which enables data engineers to deliver data solutions faster and more efficiently. This can help organisations to bring new products and services to market quickly and stay ahead of the competition.
Improved data quality: DataOps emphasises the importance of testing and validation throughout the data development process, which helps to identify and fix data quality issues early on. This can help to improve the accuracy and reliability of data, which is essential for making informed business decisions.
Greater agility: DataOps promotes a flexible and adaptable approach to data development, which enables data engineers to respond quickly to changing business requirements. This can help organisations to stay nimble and responsive in a rapidly changing business environment.
Improved collaboration: DataOps encourages cross-functional collaboration between data engineers, data scientists, business analysts, and other stakeholders, which helps to improve communication and alignment between teams. This can help to ensure that data solutions are designed to meet the specific needs of the business.
Better automation: DataOps encourages the use of automation tools and techniques to streamline and automate repetitive tasks, which helps to reduce the workload of data engineers and improve the efficiency of data-related operations.
Overall, DataOps can help data engineers to work more efficiently and effectively, and to deliver data solutions that meet the specific needs of the business in a timely and cost-effective manner.