Introduction to the series
The ability to derive insights and value from data is a key factor for the future viability of companies. In this context, the company’s data strategy as well as the technical and data skills of employees play a major role.
A prerequisite for building analytical solutions from models and algorithms is that the data is available reliably and fitting to the various needs of stakeholders in the company on the one hand, and that it is available in a form and structure that enables further processing on the other.
To meet these conditions, it is important to build an automated data pipeline:
The pipeline is a structured sequence of processes designed to efficiently and reliably route data from capture to preparation to analysis. When you want to do that, you encounter several challenges.
In a new series, we will address these challenges and show which aspects are relevant or how solutions can be built. This blog post will first give an overview of the topic before we will go into more depth on individual aspects in upcoming posts.
// 1 Data Extraction / Data Ingestion / Data Acquisition
A first step in developing a robust and efficient data pipeline is the data ingestion process. This process involves collecting and importing data from various sources. During this phase, challenges can arise that can hinder the development of a smooth data pipeline.
// Heterogeneity of data sources and data structures
Data can come from a variety of sources, including databases, files, APIs, and streaming platforms. Each source can have its own data format, structure, and flow. Therefore, it is important to implement mechanisms to overcome this heterogeneity, such as transforming the data into a uniform format or implementing adapters for specific data sources.
// Ensuring Data Quality and Integrity
In the context of data ingestion, there is a risk of erroneous or incomplete data entering the pipeline. It is therefore necessary to implement data validation and cleansing mechanisms to ensure that only high quality data enters the pipeline. This can be achieved by using data validation rules, checking data for consistency, and implementing error handling mechanisms.
// Thinking about potential requirements: scalability
With big data, scalability of the data ingestion process can become a challenge. Processing large amounts of data efficiently requires robust and powerful systems. One way to address this issue is to use scalable data processing frameworks such as Apache Hadoop or Apache Spark to process the data in parallel and distribute the workload across multiple nodes. A particular challenge in this context is the ingestion of real-time data. For streaming, special frameworks such as Apache Kafka are available to help process real-time data efficiently and minimize latency.
// 2 Data Wrangling and Data Preparation
Data wrangling refers to the process of cleaning, transforming, and integrating raw, unstructured, or unformatted data to prepare it for further analysis or use in a data pipeline.
It involves preparing the data to ensure that it has a uniform and consistent structure, missing values are addressed, inaccuracies are corrected, and potential outliers are identified and addressed. The focus is on data quality and preparing the data for further steps such as data analysis or machine learning. The purpose of Data Preparation is to prepare the data so that it is optimally suited for the specific use case or analysis.
// Data transformations and formatting
In this step, the data is transformed into a form suitable for analysis. This may include transforming data into numeric or categorical values, scaling values, or creating new characteristics. The goal is to prepare the data for the desired analytical methods. For example, certain algorithms do not accept categorical variables. Sometimes it may also be useful to standardize or normalize data to best prepare it for analysis.
In addition, it may be necessary to convert data to a specific file format or make adjustments to the column or row structure or change the data organization.
// Data aggregations
To enable a higher level of analysis, data is aggregated or reduced. This can be done by grouping data by specific characteristics, summarizing data in specific time periods, or by deriving key figures. Aggregation facilitates the analysis of large amounts of data and allows patterns or trends to be identified at a higher level.
// Data validation and verification
Prepared data must be checked for quality, consistency, and accuracy to ensure that it meets analytical requirements. This includes running tests, verifying data relationships, and comparing results to expected outcomes.
// 3 Data provision for business users
Each stakeholder has different data requirements. It is important to understand the needs and expectations of each stakeholder and ensure that the data provided is relevant to their specific tasks and analyses.
// Data Access and Security
Different stakeholders have different requirements for accessing the data. It is important to ensure that data access is governed according to policies and permissions to ensure data confidentiality and security. Depending on the sensitivity of the data, different security measures such as access controls, encryption, or anonymization may be required.
// Data understanding and documentation
For stakeholders to effectively use the data provided, it is important to provide them with clear documentation and metadata about the data structure, the meaning of the fields, and any transformations.
// 4 No-code / low-code platforms as the means of choice
Software from Alteryx can significantly help build data pipelines efficiently and reduce reliance on specialized technical resources.
Alteryx Designer and Designer Cloud provide a user-friendly, graphical interface through which data processing workflows can be created by simply dragging and dropping building blocks. This enables staff without extensive technical skills to perform data preparation tasks.
Specific features and functionalities can be accessed:
Workflow automation: Once created, workflows can be executed repeatedly as new data becomes available, or they can be scheduled to process data updates automatically.
Numerous built-in connections to various data sources, including databases, cloud storage, APIs, and more. This facilitates access to multiple data sources and reduces the time typically required for the connection establishment and data preparation process.
Predefined data transformations and operations, such as filtering, merging, aggregating, cleansing, converting data types, etc. These predefined functions greatly simplify data manipulation and reduce development time.
Scalability: Alteryx Designer and Designer Cloud are able to handle large amounts of data and deal with extensive data processing workflows. This enables organizations to handle complex data preparation tasks and efficiently scale their data analysis processes.
Do you have questions about Data Preparation or Alteryx? Contact us!