An overwhelming 97% of data engineers report burnout in their day-to-day jobs, citing repetitive tasks and fixing errors in the data lifecycle as the most common reason. For most organizations today, data arrives in droves from multiple locations. Manually loading these data for integrating these data into one central location for decision-making is far too time-consuming. Applying automation improves the efficiency of these tasks and reduces the burden on data engineers. It also reduces the risks associated with manual errors, improves quality and consistency, and leads to better data for use.
This article explores how automating data integration helps reduce engineering burnout while improving your data quality for informed decision-making.
The Architecture of Automated Data Integration
With the explosion of data and data sources, manual ETL became insufficient and birthed the need for a more efficient method. Automation answered the need for continuous data delivery and real-time data processing and helped eliminate the time-consuming, error-prone, and repetitive tasks involved in manual data integration.
Automated data integration informs an organization’s overall data management strategy. Data management refers to the coordination of data ingestion, transformation, processing, loading, and analytics to help maximize data value while ensuring compliance with data laws and regulations. By employing automation along each stage of the data management lifecycle, from data collection to consumption, organizations improve productivity and data quality and generate insights for faster decision-making. This process may involve using ETL tools or data integration platforms to ingest data in streams or batches and then storing it in a staging or storage area like a data warehouse or data lake for processing and analysis.
Analysis involves using data mining or machine learning algorithms to identify helpful patterns and relationships with the data. Business stakeholders or end-users consume the analyzed data via dashboards, visualizations, or reports. Data integration platforms like StreamSets enable complete automation of your data processes, from ingestion to delivery through smart data pipelines.
Automating Data Ingestion
Data ingestion involves collecting and importing data from multiple sources into a centralized storage location like a data warehouse or data lake for Business Intelligence(BI) and analytics for decision-making. However, because companies today utilize multiple data sources — even hundreds or thousands — in various formats, it becomes hard to keep up using manual processing methods. In the past, engineers manually performed repetitive imports, cleaning, and uploading tasks on every newly arrived data to these data before loading them to new locations. However, the repetitiveness and slow pace meant more time inefficiency, room for errors, and slower time for analytics.
With automation, the laborious and repetitive data ingestion process becomes more efficient, freeing time for engineers to create algorithms and obtain insights from newly loaded data. Data ingestion may involve automating processes like:
- Data collection: StreamSets can automatically ingest streaming and batch data from data sources like IoT devices, CRM systems, social media, websites, etc. to reduce the extra time spent performing it manually.
- Data preparation and transformation: As data ingestion occurs, ETL tools clean and prepare data to produce high-quality data. The StreamSets Transformer can perform stream processing using a variety of techniques, including windowing, aggregation, and filtering. Users can define data processing rules and transformations that are applied as data flows through the system, enabling real-time data analysis and processing. The engine can also handle complex data transformations and can integrate with other tools and technologies to support advanced analytics and machine learning.
- Data loading: Data loading involves loading your newly transformed and cleaned data into a data lake or data warehouse for further use.
Automating Data Processing
Data processing involves transforming data from its raw, unusable form into clean, high-quality formats for tasks like Machine Learning (ML), BI, and analytics. Data processing can occur regularly in batches (batch processing), in real-time called stream processing, or distributed among multiple servers as seen in distributed processing. It is a 6-step process from collection to storage which involves processing tasks like checking for errors, data formatting, segregation, removal of bad data, and aggregation to provide high-quality data. These tasks are time-consuming and laborious when performed manually, which reduces work efficiency and leads to errors.
Thanks to automation tools, data engineers can now automate data processing tasks:
- Data collection: This first step may utilize ingestion tools to collect raw data from data sources.
- Data preparation: Engineers can write scripts to automate data cleaning tasks like changing data fields, removing null and incomplete or poorly formatted data, validation, type conversion, and standardization — or save time and increase accuracy by using low/no code solutions like StreamSets.
- Data processing: Prepared data undergoes various processing techniques using ML algorithms or Artificial Intelligence (AI). The types of processing depend on the end use case for the data.
- Data storage: Newly processed data are transported to storage locations like data warehouses or data lakes to aid easy retrieval access and retrieval.
Automating Data Management
Data management refers to the tasks and processes involved in the data lifecycle, from data collection to processing and analytics. Data management helps ensure compliance while data moves between locations while producing high-quality data for use in analytics. It involves the following time-consuming, repetitive tasks that become more efficient and accurate with automation:
- Data synchronization: Employing automation for synchronization usually occurs after loading. With automated tools like StreamSets CDC, businesses save the resource-intensive cost of reloading complete datasets as CDC captures and loads only the data that has changed since the last loading.
- Data migration: Data migration tools can be cloud-based, on-premise, self-scripted, or open-sourced. These tools perform the ETL tasks of extracting, transforming, and loading into a new target destination.
- Data quality assurance: These tools help detect data anomalies, outliers, and duplicates to help deliver high-quality data to storage locations. For example, StreamSets pipelines have built-in early warning and anomaly detection and can be used with Apache Griffin, Kafka, and Spark to help build a data architecture that reports on data quality.
- Data duplication: Duplicated data leads to data redundancy which may result in more database complexity and slower load times. Deduplicate processors can help detect and remove duplicate records from a batch. For example, you can configure the StreamSets deduplicate processor to detect duplicates from a specified field or an entire dataset.
Best Practices: Automated Data Integration Implementation
Although adopting automation provides massive benefits for organizations, automating without taking note of how it affects and aligns with business decisions and objectives can create pitfalls for organizations in the future. Organizations choose automation to either fulfill one or more of these three business objectives:
- Save costs and improve operational efficiency.
- Improve end-user experience
- Improve the quality of digital products and services
Therefore, it is crucial to know when and how much to automate and how to prioritize automation in your business.
Knowing When To Automate
Knowing when to automate can save time on repeated data tasks that reduce work productivity. Here are some ways to know when to introduce automation to a process:
- Repetitive, scheduled tasks: Most data management processes like loading, processing, and transformation occur multiple times over the day, especially for data-intensive organizations. Automation tools can run scheduled jobs to help perform these tasks to improve work productivity.
- Processes involving multiple technologies: The movement of data in and out of data sources and target destinations usually involves numerous technologies. For engineers, the time and tedium of configuring these technologies reduces productivity and slows progress. Instead, automated tools can use triggers to help move these data between these technologies.
- Voluminous and human-error-prone tasks: Data arrives in mass amounts, sometimes petabytes daily, and having engineers perform manual tasks on them increases the risk of human errors, reducing data quality and affecting analytics and BI results.
Prioritizing Automations
Although the benefits of automation mean massive rewards in terms of efficiency and quicker time to insights, its adoption can be often challenging, especially for smaller to medium scale organizations, and hence limited only to prioritized tasks. Additionally, as more organizations adopt automation, the requests to automate more tasks arise, so a need to manage these demands to prevent the error of automating everything, as not every process needs automation.
One can decide on what process to automate by taking the following steps:
- Establish an automation governing body that uses a scorecard to evaluate automation needs. Automation scorecards should take into considerations factors like:
- Rule-based or human-judgment process: Are subsequent steps in the task primarily dependent on human judgment, or can they progress without significant human input by only following a set of rules?
- Operational impact: This factor helps evaluate the impact of automation on operations. How often do we perform this task each month? What volume is processed? If approved, will this automation significantly improve work productivity, and if yes, how much?
- Scalability of automated processes: Are processes coordinated such that automated scripts become applicable to other moving parts? Is the application architecture layered carefully to promote communication between layers? By tying these processes together, they become linked, making automation efforts go a long way.
- Organizational impact: Automating processes that only affect a single action in an organization produces little impact against those applicable to multiple functions, which provides more effect.
- Evaluate and manage automation by routinely reviewing the impacts of current automation and adjusting automation scorecards to changing business requirements and needs.
Automated Data Integration Tools
Data integration is crucial for most data-driven organizations looking to harness the value of their data. The data integration process involves a series of repetitive and time-consuming tasks. Performing these tasks manually reduces work productivity and can slow the time to develop insights. However, thanks to automation tools, several steps in the data integration process can proceed quickly and efficiently without human error-prone risks. The use of data integration tools, for example, to aid in the collection, transformation, and loading of data as it moves between locations.
StreamSets data integration platform lets organizations seamlessly integrate their data. For example, in the case of data drift which can invalidate ML models and produce incorrect analysis, StreamSets provides automatic data drift detection, eliminating the time-consuming task involved with manual debugging or fixing broken pipelines. Additionally, with StreamSets reusable pipeline fragments, constructing new data pipelines can proceed by simply reusing these fragments instead of the lengthy development time involved in starting from scratch each time.
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