TOP 10 Data Engineering Tools for 2024

The global LinkedIn job search for "data engineer" found 46,354 job listings, underscoring its significant role in the workforce. As data volume and complexity expand, so does the need for proficient experts. Data engineers are responsible for designing, building, and maintaining the infrastructure and systems that enable organizations to collect, store, process, and analyze large volumes of data. However, without sophisticated data engineering tools, it's simply impossible. Here are the top 10 tools for data engineering that will help industry experts boost performance, scalability, and innovation in 2024.

In this post, we aim to clarify the following: what is data engineering, the top tools for data engineering, data engineering tasks and responsibilities, and the key data engineering skills.

Data proliferation has reached staggering heights, with the latest estimates indicating a daily creation of 328.77 million terabytes. Remarkably, 90% of the world's data has emerged in the past two years alone, marking an exponential surge in information generation. Projections suggest that data generation will exceed 181 zettabytes by 2025, signifying a remarkable 150% increase since 2023.

Raw data presents a valuable source for businesses striving to succeed in today's highly competitive world to understand market trends, enhance business processes, and make informed decisions. It is where data engineering steps in. It is the foundation for successful data analysis, business intelligence, and artificial intelligence applications.

Data engineering is the discipline focused on designing, building, and maintaining the infrastructure and systems necessary to handle large volumes of data effectively. Data engineering tasks involve data collection, storage, processing, integration, and analysis. Data engineering transforms raw data into actionable information, empowering organizations to make informed decisions and achieve their strategic objectives.

What is the difference between data engineering and data science? It is important not to confuse data engineering with data science. While data engineering focuses on building and maintaining data infrastructure to enable efficient data processing, data science focuses on analyzing data to extract insights and drive decision-making. Data scientists are responsible for formulating hypotheses, designing experiments, and applying statistical and machine-learning techniques to analyze data and extract actionable insights. They develop models to predict future trends, identify patterns, and solve complex business problems. However, both fields are essential components of a data-driven organization, working together to harness the full potential of data for business value.

Here are some of the top tools used in data engineering:

Python has a straightforward syntax that promotes comprehension and aids in the learning process. This language allows developers with varying skill levels to create prototypes and durable solutions for multiple domains swiftly. Many libraries support Python, including data analysis, web development, machine learning modules, or frameworks that assist in time-efficient coding tasks. Additionally, its cross-platform compatibility makes it suitable for use on various types of operating systems, making it an optimal choice as the primary programming language required by many projects across different fields.

Challenges may arise for performance-critical applications or high-concurrency scenarios due to Python's interpreted nature and Global Interpreter Lock (GIL), which limit its performance capabilities. Furthermore, ensuring compatibility and avoiding conflicts in Python projects necessitates meticulous attention when managing dependencies and package versions, potentially making the process burdensome.

Python is a useful tool for extracting sales data from diverse sources like databases and APIs, conducting transformations utilizing libraries such as Pandas to clean and manipulate the data, and then storing it in a centralized warehouse or storage system; this would be applicable in retail businesses.

SQL, or Structured Query Language, stands as one of the fundamental data engineer tools, offering many advantages and disadvantages for managing and querying databases. On the positive side, SQL boasts faster query processing, enabling swift retrieval and manipulation of large datasets. Its user-friendly syntax, featuring basic keywords like SELECT and INSERT INTO, streamlines data retrieval without necessitating extensive coding skills. SQL's standardized language also fosters global uniformity in database management, supported by its extensive documentation and longstanding establishment.

Moreover, SQL's portability across platforms and integration into various applications enhance its versatility and accessibility, while its built-in data integrity constraints ensure accuracy and prevent duplication. For example, with SQL Connector for Jira, users can export Jira data to various SQL Databases, including PostgreSQL, Microsoft SQL Server, MySQL, MariaDB, Oracle, and H2 with no code, streamlining operational reporting, analysis and automation capabilities.

Although SQL is a widely used language for data management, its interface can be complex and may come with potential costs. Furthermore, these databases need to be optimized for real-time analytics, and hardware upgrades may become necessary as the volume of stored data increases over time.

SQL is useful in scenarios such as managing transaction data in banking systems. Organizations can design relational database schemas to store and query structured data efficiently, ensuring data consistency and integrity through features like constraints and transactions. SQL facilitates the ingestion of data from diverse sources, enabling the transformation and loading of transaction data into the database tables. Its powerful security mechanisms protect sensitive financial information, while its powerful querying capabilities empower users to extract actionable insights through complex analysis and reporting.

PostgreSQL, originally named POSTGRES, stands out as a powerful open-source object-relational database management system (ORDBMS) renowned for its rich feature set and compliance with standards. Notably, it offers diverse data types, including advanced options like arrays, JSON, and geometric types, catering to diverse data modeling needs. Moreover, PostgreSQL's adherence to the ACID principles ensures transactional reliability, while its utilization of Multi-Version Concurrency Control (MVCC) facilitates high concurrency without compromising data integrity. Advanced querying capabilities, such as support for recursive queries and window functions, empower users to execute complex data manipulations efficiently. Additionally, PostgreSQL's indexing techniques, encompassing B-tree, hash, and GiST, enhance query performance, making it a preferred choice for demanding workloads.

Despite its strengths, PostgreSQL does have some limitations. The interpretive nature of SQL engines in PostgreSQL may lead to suboptimal CPU utilization and performance issues, especially with complex, CPU-bound queries. Furthermore, its implementation may require more time than other databases, potentially affecting project time-to-market. While PostgreSQL excels in handling large-scale applications and offers horizontal scalability options, comparatively lower reading speeds may affect its performance in read-intensive scenarios.

PostgreSQL will be beneficial for a retail company that needs to efficiently manage and analyze its sales data to gain insights into customer behavior, product performance, and market trends. PostgreSQL can design a relational database schema to store various aspects of sales data, including customer information, product details, transaction records, and sales channels. The schema can be optimized for efficient querying and analysis, with appropriate indexing and normalization to ensure data integrity.

BigQuery, Google Cloud's fully managed, serverless data warehouse solution, stands out for its scalable architecture, real-time analytics capabilities, SQL interface, machine learning integration, and support for geospatial analysis, all of which streamline data analysis processes and facilitate collaboration across projects and organizations while maintaining strong data security. Additionally, it offers a cost-effective pay-as-you-go pricing model, exceptional performance, seamless integration with other Google Cloud services, and even more with third-party plugins. For instance, the BigQuery Connector for Jira empowers non-technical users to load Jira data into BigQuery for enhanced data management.

However, users should be aware of the potential challenges of this one of the commonly used data engineering technologies, including the learning curve associated with mastering of specific BigQuery features, cost management considerations to avoid unexpected expenses, limited customization options compared to self-managed solutions, and potential data egress costs when transferring data out of BigQuery to other services or locations, underscoring the importance of careful optimization and management practices to maximize its benefits and minimize drawbacks.

BigQuery, as a top data engineering tool, may be helpful for a telecommunications company seeking to analyze customer data to enhance customer experience, optimize service offerings, and reduce churn rates. Various customer data sources, including call records, internet usage logs, billing information, and customer support interactions, are integrated into BigQuery. It can involve streaming data in real-time from call centers, batch uploads from billing systems, and API integrations with third-party service providers.

Tableau is a leading data engineering and visualization tool renowned for its intuitive user interface, drag-and-drop features, actual analysis capabilities, collaboration capabilities, mobile support, and advanced visualization techniques. It’s famous for its ease of use, data blending capabilities, robust community support, flexibility in customization, and scalability to handle large datasets. Its superpower lies in its diverse data connectivity options. For example, with Alpha Serve’s enterprise-grade Tableau data connectors, you can easily fetch data from Jira, ServiceNow, Shopify, Zendesk, and monday.com.

Despite being one of the best data engineering tools, this solution is costly, may lead to performance issues with complex datasets, has limited data preprocessing functionalities, and has a long learning curve for mastering advanced features. Despite these drawbacks, this data engineer manager remains popular for organizations seeking powerful data visualization solutions across various industries.

The integration of Tableau Connector for Zendesk offers a powerful data engineering framework for analyzing and optimizing customer service performance. Organizations can gain comprehensive insights into customer service performance, enabling data-driven decision-making and continuous improvement. Zendesk Connector automates data retrieval and integration processes, saving time and effort for data engineers and analysts. Additionally, real-time monitoring capabilities ensure organizations respond promptly to customer service issues, improving customer satisfaction and retention rates.

Looker Studio, formerly Google Data Studio, is a free data engineering software from Google, a visualization and reporting tool boasting features like diverse data source connectivity, customizable dashboards, real-time data access, collaboration, and easy sharing and publishing of reports. Its advantages include cost-effectiveness, seamless integration with Google products, user-friendly interface, customization options, and cloud-based accessibility.

However, Looker Studio may encounter challenges with handling large datasets or complex data transformations, lacks some advanced analytics features found in paid tools, can experience performance issues with more extensive reports or specific data sources, and may entail a learning curve for mastering all its functionalities. That’s why organizations with more complex data analysis requirements may need to supplement it with additional tools to address its analytical limitations effectively. For example, with third-party connectors.

A software development company aiming to improve project management efficiency and effectiveness may greatly benefit from Looker Studio. Looker Studio Connector for Jira allows the export of Jira data into Looker Studio, extracting project-related information, including task status, sprint metrics, team performance, and issue resolution times. Using Looker Studio's intuitive interface, data engineers create interactive dashboards and reports that provide comprehensive insights into project management performance. These dashboards can include metrics such as sprint velocity, team productivity, issue backlog trends, and project completion rates.

Power BI, Microsoft's business analytics service, offers a suite of tools for data engineering tasks like data aggregation, analysis, visualization, and sharing. Its features include versatile data connectivity, interactive visualizations, robust data transformation and modeling capabilities, AI support for insights, customization options, and collaboration features. Advantages include seamless integration with Microsoft products, a user-friendly interface, regular updates, scalability, and strong security. However, challenges include potential performance issues with large datasets, a learning curve for advanced features, subscription fees for Pro and Premium versions, limited data refresh in the free version, and dependency on other Microsoft products for integration. Fortunately, these disadvantages can be mitigated using Power BI Connectors.

With Power BI Connector for QuickBooks, Power BI transforms into a robust data engineering platform for financial performance analysis. Once integrated, Power BI data engineers can create interactive dashboards and reports with metrics, including revenue trends, expense breakdowns, profitability analysis, and cash flow forecasts. In addition, Power BI Connector for QuickBooks actual data retrieval capabilities offers real-time monitoring of KPIs, enabling immediate identification of financial trends and anomalies.

MongoDB, a widely used document-oriented, cross-platform database, stands out for its flexible schema design, scalability through segmentation, optimized performance, powerful query language, robust community support, and versatile deployment options. Advantages include schema flexibility for evolving data requirements, seamless scalability with sharding, optimized performance for high-speed data access, robust community and ecosystem support, and comprehensive security features.

However, MongoDB as a data engineering tool faces limitations such as the absence of support for joins and complex transactions, data integrity challenges due to varied data types, potential complexity in schema design, increased memory usage from data redundancy, and constraints on document size.

MongoDB may help a social media platform to analyze and process ongoing user interactions, such as likes, comments, and shares, to personalize user experiences and improve engagement. It can store and transform user interaction data using aggregation pipelines or change streams. MongoDB feature to handle real-time data ingestion and processing enables the platform to derive immediate insights from user interactions, leading to more timely and relevant user experiences. Its horizontal scalability and sharding capabilities ensure that the platform can handle growing volumes of user data and increasing traffic loads without sacrificing performance.

Apache Spark, an open-source framework renowned for big data processing and machine learning, boasts exceptional speed and performance, versatility across multiple data engineering programming languages, resilience through RDDs for fault tolerance, comprehensive libraries, detailed documentation, and strong community support. Its pros encompass accelerated access to big data workloads and advanced analytics capabilities.

Despite its advantages, Apache Spark faces challenges such as the need for manual optimization due to lacking automatic optimization processes, potential cost concerns arising from significant memory requirements for in-memory computation, and a limitation in real-time processing, relying on micro-batch processing instead.

Imagine a retail company willing to analyze customer behavior in real-time to personalize marketing campaigns and optimize inventory management. Using its Spark Streaming module, Apache Spark can be used to ingest streaming data from various sources, including website clickstreams, transaction logs, and social media interactions. Then, the data undergoes actual transformation using Spark's powerful APIs and libraries. Apache Spark enables real-time analytics like identifying popular products, predicting demand, and detecting fraud or unusual activity. These insights can be further used to trigger automated ongoing actions, such as sending personalized offers to customers based on their browsing history, adjusting inventory levels dynamically, or flagging suspicious transactions for further investigation.

Apache Kafka, a distributed streaming platform, excels in scalability, durability, and real-time processing. Its distributed architecture enables parallel data processing across brokers, ensuring low latency and high throughput. Kafka's scalability allows for the seamless addition of servers to handle growing data volumes without downtime, while its replication mechanism ensures message durability and prevents data loss.

Additionally, Kafka's integration capabilities with technologies like Apache Spark and Elasticsearch enhance its functionality, and a large user community supports it for ongoing development and support. Despite its advantages, Apache Kafka has some cons, such as complexity in implementation and configuration, resource intensiveness, security considerations, and user learning curve.

One of the use cases for Apache Kafka is detecting fraudulent activities in real-time to safeguard against financial losses and protect its customers' assets of a financial institution. This tool will serve to ingest vast streams of transaction data from banking transactions, ATM withdrawals, online payments, and credit card transactions. Its event-driven architecture allows for seamless processing of events as they occur, ensuring timely detection and response to suspicious activities. Kafka's durability ensures message persistence, preventing data loss even during system failures. Finally, it integrates seamlessly with analytical tools like Apache Spark and Elasticsearch to analyze and visualize detected fraud patterns further.

Here are some main considerations to keep in mind when evaluating data engineering tools:

Performance and Scalability: Prioritize tools with high performance capabilities, essential for managing large datasets and real-time processing demands, while ensuring scalability to accommodate future data growth.

Integration Flexibility: Assess the tool's compatibility with existing systems and technologies within your organization's data ecosystem, enabling streamlined workflows and improved efficiency through seamless integration.

Customization Level: Determine how the tool can be customized to meet your organization's requirements and workflows. Customization options can enhance the tool's flexibility and adaptability to evolving business needs.

Cost Structure: Evaluate the total cost of ownership, including licensing fees, maintenance costs, and any additional infrastructure requirements. Consider both upfront costs and long-term expenses associated with using the tool.

Security and Compliance: Ensure the tool implements encryption, access controls, and auditing capabilities to protect against unauthorized access and ensure regulatory compliance with industry standards such as GDPR or HIPAA.

Future Proofing: Choose tools continuously updated and supported by the vendor to ensure compatibility with future technologies and industry standards. Future-proofing your data engineering stack can prevent costly migrations or disruptions down the line.

Data engineers are crucial in managing, processing, and analyzing large volumes of data within organizations. Their responsibilities encompass a wide range of tasks essential for ensuring data quality, accessibility, and usability. Here are the core data engineering responsibilities:

Data engineering necessitates a varied skill set to manage and process vast amounts of data efficiently. Here are some essential data engineering skills required to excel in their roles:

Data engineers are crucial in developing, constructing, and maintaining an organization's infrastructure for data generation, storage, and processing. They utilize various data engineering tools and technologies such as Apache Spark, Hadoop, Apache Kafka, and SQL databases to extract, transform, and load data efficiently. Integrating these technologies with our connectors can significantly contribute to the success of data-driven initiatives within organizations. If you want to advance your company's data analysis and data engineering experience, explore the latest data engineering tools and integration apps available and consider incorporating them into your IT infrastructure.