Author | Li Chen
Editor | Debra Chen
Data preparation is critical to driving effective self-service analytics and data science practices. Today, businesses know that data-based decision-making is the key to successful digital transformation, but to make effective decisions, only trusted data can help. As the volume of data and the diversity of data sources continue to grow exponentially, Achieving this is increasingly difficult.
Today, many companies invest a lot of time and money into integrating their data. They use data warehouses or data lakes to discover, access and consume data, and leverage AI to drive analytics use cases. But they quickly realized that processing big data in the lake warehouse was still challenging. Data preparation tools are the missing component.
What is data preparation and what are the challenges
Data preparation is the process of cleaning, standardizing, and enriching raw data. This makes the data ready for advanced analytics and data science use cases. Preparing data for moving it to a data warehouse or data lake requires performing several time-consuming tasks, including:
- Data Extraction
- Data cleaning
- Data normalization
- Data external services
- Orchestrate data synchronization workflows at scale
In addition to the time-consuming data preparation steps, data engineers also need to clean and normalize the underlying data, otherwise, they will not understand the context of the data they want to analyze, so small batches of Excel data are often used for this purpose. But these data tools have their limitations. First, Excel cannot accommodate large data sets, does not allow you to manipulate the data, and cannot provide reliable metadata for enterprise flows. The process of preparing a data set can take anywhere from weeks to months to complete. The survey found that a large number of companies spend up to 80% of their time preparing data, and only 20% of their time is used to analyze the data and extract value.
Flip the 80/20 Rule
As unstructured data grows, data tools are spending more time than ever deleting, cleaning, and organizing it. Data engineers often overlook critical errors, data inconsistencies, and processing exceptions. At the same time, business users are demanding data in shorter and shorter times, and the need for high-quality data for analysis is greater than ever. Data preparation methods simply cannot meet the needs. Data engineers and data analysts often spend more than 80% of their time finding and preparing the data they need. As a result, they only spend 20% of their time analyzing data and deriving business value. This imbalance is known as the 80/20 rule.
So how to effectively reverse the 80/20 rule? For complex data preparation, an agile, iterative, collaborative and self-service data management method - DataOps - is needed to help enterprises greatly improve the efficiency of data preparation and turn 80/20 waste into the company's advantage. The DataOps platform enables IT departments to provide self-service capabilities for their data assets and enables data analysts to discover the right data more effectively, while applying data quality rules and collaborating better with others to deliver business in less time. value.
Providing data analysts with the right data at the right time means complex data can be prepared, data quality rules can be applied, and business value can be delivered in less time. With these enterprise-grade data preparation tools, data and business teams will:
- Reduce time spent on data discovery and preparation, and accelerate data analytics and AI projects
- Process massive structured and unstructured datasets stored in data lakes
- Accelerate model development and drive business value
- Uncover hidden value in complex data with predictive and iterative analytics
How Beluga Open Source Can Help
WhaleStudio, the open source DataOps platform of White Whale, provides a code-free, agile data preparation and data collaboration platform so that enterprises can focus more on data science analysis, artificial intelligence (AI) and machine learning (ML) use cases.
Orchestration, scheduling and OPS capabilities covering the entire process
Intelligence and automation are critical for speed, scale, and agility. Every step of data development benefits from powerful orchestration and scheduling capabilities. These capabilities will increase the speed and scale of enterprise processing data, and can also span cloud platforms and processing engines. Manage various data tasks. The unified scheduling system in the open source WhaleStudio - WhaleScheduler will help you establish a one-stop, systematic and standardized pipeline management model for data collection, processing, operation and maintenance, and services. Through unified data orchestration and scheduling, Provide services for the data consumption pipeline to make the data capability service operation process more secure, agile and intelligent.
At the same time, WhaleStudio is based on DataOps best practices to bring agility, productivity and efficiency to your environment, helping you get immediate feedback by releasing more frequently, faster and with fewer errors. The IDE and collaboration platform in WhaleStudio provide you with CI/CD capabilities out of the box, which enable you to break down development, operations, and security silos and provide a consistent experience throughout the entire data development lifecycle. picture
Introduce data
After determining the processing process, the data needs to be introduced into the data lake. Usually, data initialization is performed first, and the basic data is introduced into the lake in full, and then change data (CDC) is captured from the data source for incremental loading to achieve real-time data capture.
With WhaleTunnel, the data synchronization tool in the open source WhaleStudio, developers can automatically load files, databases, and CDC records. The cloud-native solution allows you to quickly introduce any data with any delay (batch, incremental, quasi-real-time, real-time). It's easy to use, wizard-driven and low-code, so anyone can use it right out of the box.
Ensure data is trustworthy and available
Once data is ingested into a data lake, it needs to be clean, trusted, and ready to use. Beluga's open source data integration and data quality solutions enable developers to quickly build, test and deploy data pipelines using drag-and-drop methods in a simple visual interface.
The data quality module built in the WhaleScheduler system provides a full range of data quality functions, including data analysis, cleaning, deduplication and data verification, helping users avoid the problem of "garbage in, garbage out" and ensure data Clean, trustworthy and usable. The metadata module in the WhaleScheduler system provides lineage analysis functions to help enterprises quickly analyze the situation of various data sources and targets, speed up the handover between developers and code review efficiency, and further ensure that data accuracy.
Create high-performance data processing pipelines
Once the data enters the data warehouse or data lake, data users may want to further slice and analyze the data set, and they can continue to use the visual designer of the WhaleScheduler system to build DAG logic. The data integration capabilities built into WhaleTunnel can quickly build high-performance end-to-end data pipelines using no-code interfaces, allowing developers to easily move and synchronize data between any cloud or local systems. The batch-stream integrated data synchronization method is perfectly compatible with offline synchronization, real-time synchronization, full synchronization, incremental synchronization and other scenarios, which greatly reduces the difficulty of data integration task management.
To sum up, the White Whale open source WhaleStudio suite can help enterprises solve a series of problems such as complex data integration of internal multiple data sources and multiple data systems, continuous development, continuous deployment, data capture, data unblocking, etc., accelerate the data preparation process, and comprehensively improve Ability to analyze data and build large models.
This article is published by Beluga Open Source Technology !