Modern Data Processing
Data is only as valuable as it is usable. Raw data is generated in different formats and is not validated or organized in any way. For this reason, raw data must be cleaned and translated into a form that’s usable for analysis. Fast and effective data processing is essential for a company that wishes to be data-driven and to support operations involving data. In this post, we’ll explain what data processing is and explore the changes that have created new demands on data processing.
What Is Data Processing?
The term data processing describes the standardized sequence of operations used to collect raw data and convert it into usable information. Raw data sources may include social media pages, websites and applications, POS systems, and IoT sensors. Although the method may vary depending on the data source and use case, data processing always follows a series of prescribed steps.
Data processing is a key component of the data pipeline, which enables the flow of data from a source into a data warehouse or other end destination. Elements of data processing may occur either before data is loaded into the warehouse or after it has been loaded.
Modern Data Processing
With the rise of big data, and as data processing has shifted from on-premises to cloud-based systems, the data pipeline has developed to support the needs of new technologies. Let’s look at three major changes that have created new demands on data processing.
On-premises ETL to cloud-driven ELT
When data was only coming from enterprise resource planning (ERP), supply chain management (SCM), and customer relationship management (CRM) systems, it could be loaded into pre-modeled warehouses using highly structured tables. Data was processed and transformed outside of the “target” or destination system via an extract, transform, and load (ETL) workflow. These traditional ETL operations used a separate processing engine, which involved unnecessary data movement and tended to be slow, and weren’t designed to accommodate schemaless, semi-structured formats.
When streaming data sources came into existence and businesses recognized the need to make use of the massive amounts of data being generated in a variety of formats, ETL workflows no longer sufficed. Modern data pipelines now extract and load the data first, and then transform it once the data reaches its destination (ELT rather than ETL). Modern data pipelines use the limitless processing resources of the cloud so you don’t need to prepare data before you load it. You can load the raw data and transform it later, once the requirements are understood.
Batch processing to continuous processing
Batch processing updates data on a weekly, daily, or hourly basis, ensuring good compression and optimal file sizes. But batch processing introduces latency—time between when data is generated and when it is available for analysis. Latency delays time to insight, leading to lost value and missed opportunities.
With continuous or stream processing, data is pulled in continuously, within a few seconds or minutes after the data is generated. Until recently, enabling stream processing was expensive due to the constraints of on-premises resources. But thanks to the power and scalability of the cloud, continuous processing is now achievable and affordable.
Consolidation of systems for structured, semi-structured, and unstructured data
Data is being generated in structured, semi-structured, and unstructured formats, but these data sets must be consolidated to be useful. Data silos not only slow down speed to insight but also create governance headaches. Modern data platforms include pipelines that can seamlessly ingest and consolidate all types of dataso it can be housed in one centralized, governed repository.
Modernizing Your Data Pipeline
To make use of your data when you need it, without losing valuable time, you need a modern workflow for data processing. Whether data arises from an enterprise application, a website, or a series of IoT sensors, that data must be captured and ingested into a versatile data repository and put into a useful form for the user community. With a modern data pipeline, you can manage your data more efficiently in one location, as a single source of truth.
Data Processing with Snowflake
Traditionally, data processing tasks had to be managed and maintained outside of an organization’s data warehouse. This external setup required a completely separate infrastructure and maintenance to keep up. The Snowflake platform, however, includes data pipeline capabilities as part of the basic service, minimizing the need for complex infrastructure tasks.
Snowflake accommodates batch and continuous data loading equally well. In addition, you can load various types of data, including semi-structured and unstructured data, into one central repository.
Snowpark is a developer framework for Snowflake that allows data engineers, data scientists, and data developers to execute pipelines feeding ML models and applications faster and more securely in a single platform using SQL, Python, Java, and Scala. Leveraging SQL and Python, Scala, or Java in Snowpark allows data teams to effortlessly transform raw data into modeled formats regardless of the type, including JSON, Parquet, and XML.
Snowflake also includes capabilities to consolidate, cleanse, transform, and access your data, as well as to securely share data with internal and external data consumers.
To test-drive Snowflake and explore its data processing capabilities, sign up for a free trial.