How Data Formats Impact Data Integration: ETL and ELT for Structured, Unstructured and Semi-Structured Data

While you’re likely already working with structured, semi-structured and unstructured data — the three dominant data formats — the proportions of each type of data in the mix and their significance/role in data-led business decision-making are changing rapidly.

These changes, propelled by the ever-evolving business and consumer behavior landscape, impact data integration processes such as extract, transform, load (ETL) and extract, load, transform (ELT), in terms of cost, speed, execution and outcomes.

This blog will help you as a data engineer better understand why the changing mix of data formats matters, and how your choice of data integration tool(s) will impact your outcomes. 

Not long ago, structured data — which is defined, predictable in terms of volume and easier to search and analyze — formed the bulk of an enterprise’s data. Well-defined data from internal systems such as enterprise resource planning (ERP) and customer relationship management (CRM) fed most decision-making.

Today, unstructured data — which does not follow a fixed format and is not easy to classify or categorize — is close to 80% of the mix.¹

Traditionally, most unstructured data came from internal sources, such as text files with documentation, videos or photographs.

More recently, however, an influx of streaming or real-time unstructured data is flowing in from an ever-increasing number of external sources, at an unprecedented volume, velocity and variety.

Unstructured data may come from:

• search engine queries
• social media
• customer review sites
• chatbot interactions
• instant messenger chats
• community tools such as Slack and Discord
• collaboration platforms such as Zoom and Teams
• customer service agent notes
• web forms
• customer quizzes
• surveys
• blog/website comments

In addition, you may notice a rise in semi-structured data streaming from IoT-connected devices, sensors, mobile applications and email applications. Data in this format is not arranged according to a pre-set data model or schema. It can include a wealth of information, but it cannot be stored in a traditional relational database. It includes logs, XML files and data in different file formats, such as JSON, Avro and Parquet.

Combined, these devices and apps produce vast amounts of human or machine-generated unstructured or semi-structured audio, video and text data that defines modern business interactions.

While the volume, velocity and variety of your unstructured data grows, so does its strategic significance to your business. Despite the complexities involved in managing such data, you cannot afford to ignore the treasure trove of customer and business insights it holds.

However, no single type of data will ultimately deliver the insights you need to pull ahead of the competition. The rapid growth of unstructured data does not mean structured data is any less crucial to the mix. You will find that much valuable data comes from system-generated reports, many of which are still Excel-based.

In effect, you need a combination of insights derived from different kinds of data from diverse sources. Such data is increasingly used to train machine learning (ML) models, aside from helping business users identify trends and opportunities to improve product design, customer experiences and other core business processes.

For example, you can help your marketing department understand that a combination of structured data — such as customer demographics, purchase history and feedback ratings — and unstructured data — such as customer reviews, social media posts and emails — offers a more powerful way to analyze customer sentiment and satisfaction levels. With such insights, they can then predict, with higher accuracy, which customers are at risk of churn and focus their resources on those for more efficient spending and outcomes.

You need a comprehensive data strategy built upon the seamless data integration of structured, unstructured and semi-structured data from the relevant sources. But getting there is not easy.

As a data professional, you are tasked with making sense of vast amounts of diverse data and transforming it into something useful to your business users.

However, managing multiple data formats and sources comes with its own challenges, including:

Movement

Varying data formats are typically stored differently, whether on-premises or in the cloud. Structured data is typically stored in data warehouses while unstructured and semi-structured data resides in data lakes or NoSQL databases.

Working directly on unstructured data for analysis and insights is almost impossible without a high degree of migration, rationalization and transformation. The implications on processing power, speed and efficiency can be daunting.

Storage

Unstructured data, which is hard to define and categorize, expands exponentially and unpredictably. Not all of it will be valuable to your business. Your data engineers need to discern what data should be kept, backed up or even deleted. And your metadata needs to be cataloged and updated continuously to accelerate data discovery and retrieval.

Transformation

By its very nature, unstructured data is difficult to query, edit, retrieve and integrate with other data formats. It also tends to get stale faster that structured data. This makes achieving the optimal combination of relational and hierarchical transformation challenging at an enterprise scale.

Integration

As the volume and variety of your unstructured data continues to grow, the number of sources you have is likely also increasing. Each time you add a new app or tool to your tech stack, you need to create new code and pipelines to connect incoming data.

Constantly creating new connectors in response to new apps can take up the bulk of your time and effort without a guarantee that the pipeline will be stable or serve a long-term purpose.

With the projected growth of unstructured data, these challenges will only get more complex and urgent for your company. 

Multiple data formats from ever-evolving sources are here to stay and present both an opportunity and a challenge.

You need to be able to ingest huge volumes of data from disparate sources and make sense of that information to drive business results. But at the same time, you need to do so at scale, while keeping costs down, efficiencies high and data secure.

DIY and manual approaches will only put your team in constant fire-fighting mode because they won’t be able to keep pace with the volume, variety and velocity of incoming data.

That is why artificial intelligence (AI)-powered data integration tools are emerging to optimize outcomes for multi-format data scenarios.

Not only does AI efficiently handle the challenges of unstructured data, but it also overcomes most human limitations to facilitate seamless data integration across multiple data formats.

Unified mass data ingestion

Your business data is likely spread across various siloed sources like files, databases, streaming and IoT devices. It also probably requires a combination of mass, streaming, database and application ingestion to move data efficiently and accurately for real-time processing, reporting and analytics.

Robust AI-powered data loader and ingestion tools help data engineers like you build and run complex data ingestion jobs in minutes, irrespective of data format, latency and scale. Using a combination of batch, streaming, real-time and change data capture (CDC), AI enables mass ingestion into cloud data warehouses, lakes and messaging hubs while automatically percolating changes from your source schema onto your target database or warehouse with real-time monitoring and alerting capabilities.

Seamless data transformation

Data pipelines act as the connective tissue for data integration. Because building them requires new logic and coding each time a new app needs to be connected, you cannot fit and forget them.

As a data engineer, you know that new apps with unique data formats keep appearing, and only a highly flexible and responsive approach can help integrate new sources of data on an ongoing basis.

AI-powered tools offer intelligent pre-built connectors to connect nearly any data from virtually any cloud with no code and no setup, to seamlessly land your data in the cloud. This enables you to respond quickly to new data sources and formats, saving you a significant amount of time and effort with reusable components while minimizing your risk of error and breakages.

Given the nature of unstructured and semi-structured data, you will benefit from tools that can find the structure of your data and automatically create a model to parse and transform a variety of files in the cloud or on-premises. Intelligent structure discovery capability can automatically discover and suggest data mappings and schemas even within your unstructured data. It can also generate and optimize data transformation logic in response to schema changes on the go, which is not possible manually.

The top tools also automatically direct your data for ELT or ETL processing to optimize for cost and performance. AI-powered recommendations and template-driven transformations can significantly reduce the processing power demanded by the scale of data today, greatly impacting the cost and speed of data integration.

The data integration process merges and loads the clean and transformed data into your central target data store, such as the warehouse or lake for further analysis.

AI can help you continually improve the cost, speed and effort involved by minimizing data transactions and optimizing core processes like data extraction, storage, intelligent structure discovery, transformation and data loading into target destinations.  

Using these tools, you can easily and continually integrate data from multiple formats and sources, minus the friction while staying focused on improving the quality of insights and business outcomes.

AI-powered, cloud-native ETL and ELT tools from Informatica can help you ingest, transform and integrate a variety of structured, unstructured and semi-structured data from virtually any source to the cloud.  

As part of the Informatica Intelligent Data Management Cloud (IDMC), Cloud Data Integration-Free and PayGo give data engineers an intelligent, fast, friction-free and scalable path to deliver a comprehensive data integration strategy, regardless of data format, structure, or source.

To learn more and get started with CDI-Free today, visit informatica.com/free-paygo.

¹https://venturebeat.com/data-infrastructure/report-80-of-global-datasphere-will-be-unstructured-by-2025/