The field of data engineering is rapidly evolving, driven by changing industry demands. In 2024, the global big data and data engineering services market was at USD 75.55 billion. Moreover, experts believe it is growing with a CAGR of 17.6%. 

Companies of all sizes have huge amounts of data that need effective evaluation to gain valuable insights and answer critical business questions. Data engineering tools play a primary role in supporting such processes and making it easy for data engineers to inspect the available data reliably. 

In this article, we will also focus on some of the best practices data engineers can adopt to gain insights from their data and highlight some of the best tools they can use for their data workflows. We will also address some common challenges for implementing advanced data engineering tools to streamline data workflows within businesses of all sizes.

Before we move forward, let’s understand what data engineering is!

What is Data Engineering?

Data engineering is the process of designing and building systems that allow data engineers to collect and analyze raw data from multiple sources and formats. Data engineering also encompasses data quality and data access assurances. 

With data engineering, data science professionals can gather valuable insights from raw, structured, semi-structured, and unstructured data. It is, therefore, the foundation that enables data scientists to perform tasks essential to maintaining the efficiency of data workflows. 

Types of Data Sources 

Data comes in many forms that are useful for various processes within an organization. 

Here are some common types of data sources data engineers work with:

1. Databases

• It is the most traditional type of data source in business intelligence. 
• Businesses use various databases, including MS Access, Oracle, DB2, SQL, MySQL, Amazon, and SimpleDB. 

2. APIs

• APIs (Application Programming Interfaces) serve as a badge between software applications, enhancing data sharing and communication.
• It also enables seamless integration with cloud platforms, web services, and other software, allowing businesses to gather real-time data on demand. 

3. Flat Files

• These are simple texts that store data in text formats in a structured or semi-structured manner. 
• Examples of flat files include XML/JSON formats and Excel spreadsheets, which are useful for sharing data across multiple systems. 

4. Streaming Data

• This data is continuously generated and transmitted from different sources, such as IoT devices, sensors, and live feeds, often in real time. 
• These data types are critical for applications that demand immediate analysis and response, such as tracking network security or managing automated systems. 

5. Cloud Services

• Cloud services have revolutionized the way data is stored, managed, and accessed across different sources. 
• Providers like AWS, Google Cloud, and Azure offer scalable storage solutions for storing vast amounts of data remotely. ‘

6. Manual Input

• Manual data input involves operators or users directly entering data into systems; it is necessary when automated data collection is not possible. 
• It is prone to human error, which can affect the overall data quality. 

Now, let’s look at some of the top advanced data engineering tools businesses can use to automate their data workflows. 

Top 6 Data Engineering Tools 

Data engineers use tools to automate their data engineering tasks. 

Here are some of the best tools for data engineering:

1. Fivetran

Fivetran is a comprehensive ETL tool that efficiently collects data for applications, servers, and websites. It also effortlessly moves data from its native state to data warehouses and then transfers it to other tools for analytics, marketing, and warehousing purposes. 

2. dbt

dbt is a command-line tool empowering data engineers to transform their data in their data warehouses using SQL. However, it does not provide any extraction or load operations. The tool allows businesses to complete their transformation processes with optimal orchestrations easily. 

3. Snowflake

Snowflake’s unique data architectures deliver high performance and improved scalability to meet the data needs of modern organizations. With Snowflake's data engineering capabilities, data engineers can scale their workloads up and down. It is an ideal platform for data warehousing, data engineering, and developing data applications. 

4. Apache Spark

Apache Spark is an open-source unified analytics platform for big data. It is a data processing framework that swiftly performs tasks on large datasets and effortlessly distributes data across multiple systems. Therefore, it is an appropriate tool for big data and machine learning, which demand large computing power. 

5. Apache Kafka

Apache Kafka is one of the primary data engineering tools used for developing real-time streaming data pipelines and applications that effectively adapt to data streams. Streaming data is generated continuously with thousands of data sources, and platforms like LinkedIn use Kafka extensively. It has a significant role in analyzing networks between people on the platform. 

6. Looker

Looker is business intelligence (BI) software that enables business professionals to visualize data. It is a popular tool among data engineers due to its LookML layer, a language for describing dimensions and data relationships in an SQL database. The LookML layer enables data engineers to make it feasible for non-technical users to make full use of their data. 

Let’s examine some of the common challenges and benefits of data engineering tools in this modern data-driven environment.

Challenges of Data Engineering

Data engineering is an emerging trend within modern businesses, and the demand for data engineers will grow by 15% from 2019 to 2029. Therefore, reducing risks within data engineering is critical for prospects. 

Here are some challenges for data engineering:

1. Event-driven Architecture

• Transitioning from batch processing to an event-driven architecture demands a complete restructuring of data pipelines.
• It comes with a high cost, complexity, and other maintenance challenges.

2. Operational Overheads

• Maintaining the efficiency of the messaging infrastructure for a real-time logistics tracking system is expensive for Kafka specialists. Kafka is a major messaging and delivery tool for data engineering, with 58% of data engineers using this platform regularly. 
• Businesses need to reallocate resources for such tasks that significantly enhance the budgets of data teams. 

3. Insufficient Resources

• The lack of sufficient resources within small and medium businesses makes it difficult for them to develop and maintain effective data pipelines. 
• Implementing a scalable data lake without appropriate resources can lead to suboptimal configuration, which may drastically affect a business's performance and cost.

4. Poor Data Quality

• Upstream data quality issues prevent data engineers from efficiently delivering quality data to their customers. 
• Data ingestion without proper cleansing or validation mechanisms leads to inaccurate analysis, disrupting data pipelines.

5. Legacy Systems

• Moving a legacy SQL-based reporting system to an advanced dashboard showing real-time analytics requires data engineers to overcome compatibility issues.
• It leads to severe technical debt, reducing agility and innovation. 

Benefits of Data Engineering Tools

Modern businesses depend on data engineering tools to optimize data workflows to enhance overall decision-making processes. Here are some benefits of modern data engineering tools:

• Tailored Customer Experiences: Advanced data engineering tools play a critical role in developing personalized customer experiences. By using appropriate data collection and analysis methods, businesses gain valuable insights into user preferences and behaviors. 
• Real-Time Data Processing: Real-time data processing is a major benefit for data engineering. Traditional data processing methods mostly use batch processing. On the other hand, real-time data processing allows organizations to collect and process data in real time, enabling better business decision-making. 
• Improved Scalability: Advanced data engineering tools allow organizations to upscale their data processing capabilities according to rising data volumes. This benefits organizations by enhancing speed and accuracy in data-driven decision-making. 
• Streamlined Costs: Advanced data engineering tools allow businesses to reduce their overall costs by optimizing their data processing workflows. Businesses minimize time and resources by developing efficient data pipelines and automated data processing, which streamlines costs for business operations. 
• Enhanced Decision-Making: Modern data engineering tools allow businesses to process and analyze big data in real time. 
• Better Data Governance: Tools for data engineering also allow businesses to comply with specific regulations and standards necessary for building data pipelines and meeting security requirements at all costs. They enable them to have an efficient data management operation. 

Let’s see how QuartileX streamlines your data engineering demands with its cutting-edge automated solution. 

Data Engineering with QuartileX

At QuartileX, we provide tailored solutions for all your data engineering needs, helping you gain valuable insights from data and secure long-term success. 

Here are some of the services that we provide to data engineers:

• We build a robust and scalable pipeline for AI-ready data with efficient storage, quality, and integration. 
• We optimize extract, load, and transform (ELT) using orchestrations, cost-effective transformations, and incremental ingestion. 
• We leverage industry-leading tools like Hevo Data and dbt to reduce infrastructure overhead. 

Next, we have highlighted some of the best practices relevant to data engineering practices to be future-ready in the data-oriented world. 

Best Practices for Data Engineering

Maintaining optimization for data engineering tools is key to securing the long-term success of an organization. 

Here are some data future-ready engineering practices to enhance data quality and efficiency in data workflow:

1. Adopting A Data Products Approach

A data product is an application or tool that processes data and generates valuable insights, enabling businesses to make valuable future decisions. 

To adopt such practice, let’s see what are the principles for such application:

• Effective product delivery processes: Integrate all data engineering practices that are important for delivering quality data products regularly. 
• Improvement processes: Appropriate monitoring and validation of data quality, including SLA agreements that align with interface availability and data freshness. 
• Product Management: This includes all the necessary processes and people to define key performance indicators (KPIs) for a data product.

2. Appropriate Collaboration While Developing Data Products

Team members within an organization must proactively collaborate and contribute to each other’s work. Appropriate use of tools that enable safe development in an isolated environment is vital to maintaining overall data integrity. 

3. Robust Recovery From Errors

Even experienced data engineers encounter errors and bugs. Rapid data movement requires efficient root-cause analysis and fast recovery from quality issues. 

To attain such efficiency, data engineers require systems that quickly identify the root causes of issues and deploy strategies to fix them. 

4. Enabling Continuous Delivery Of Quality Data With CI/CD For Data

Data engineers need to ensure data quality to achieve optimal development and deployment cycles. It allows them to identify and prevent any future errors within a data pipeline. 

Strategies like hook and pre-merge validations are necessary to test new data before it becomes erroneous and becomes a part of production. This concept is the CI/CD approach, an appropriate tool for data validation. 

5. Data Versioning

Data versioning is a core enabler for best practices in data engineering. It refers to a system in which each version of data has necessary changes. 

Here’s how it helps data engineers:

• Enables collaboration in each team to get a copy of their data. 
• It allows enhanced reproducibility to data engineers. 

Discover valuable insights and upscale with data engineering solutions

Conclusion

Data engineering is constantly evolving, and using the right tools and best practices to stay competitive is fundamental for businesses to be future-ready. Data engineering tools have critical roles in optimizing data workflows and ensuing real-time analytics for informed decision-making. Investing in data engineering solutions can improve a business's overall scalability and performance with streamlined costs.

At QuartileX, we empower businesses with robust data engineering solutions that allow for effortless data integration and effective real-time data analytics. With the rapid growth of data, it is critical to invest in the right data engineering tools to prepare businesses for a data-driven future. 

Ready to become future-ready with QuartileX’s well-crafted data engineering solutions? Get in touch with QuartileX’s data experts today to take your business to the next level.