Getting Data Ready for Data Science with Delta Lake

Machine learning algorithms have progressed through many advancements over the past decade. Despite these innovations, data scientists still experience many challenges when building data science applications.

Data scientists spend most of their time collecting and cleaning data. Then, when an application starts, they expend plenty of effort keeping it running.

Delta Lake is an open-source project built to simplify the data preparation process and provide reliable data for successful data science operations. Delta Lake is a storage layer that improves data lake reliability, enabling data scientists to harvest reliable data for their data science operations. It acts as a transactional layer on top of your data lake storage to facilitate reliable data pipelines for dataset management. It achieves this reliability by ensuring transactions are ACID (atomicity, consistency, isolation, and durability) compliant, employing unified batch and stream processing, and other measures.

Using Delta Lake, data science teams can create high-quality data ingestion pipelines and roll back errors when necessary. This ability increases their chances of having successful data science applications.

In this article, we’ll explore the data science life cycle, data engineering’s importance for successful data science, and how Delta Lake helps prepare data for analytics. Then, we’ll discuss how to prepare your data for data science applications using Delta Lake, so you can experience for yourself just how easy it is to streamline your data science processes and improve reliability.

Preparing Data for Data Science Applications

A typical data science process takes four steps:

Step 1: Getting Raw Data

The data science process starts with raw data. This data, which data scientists obtain from the environment, is full of noise, inconsistencies, and outliers. You need to ensure governance, so this data meets any necessary standards — for example, the California Consumer Privacy Act (CCPA) or General Data Protection Regulation (GDPR) privacy compliance rules. Data scientists use tools to collect, stream, or store the raw data, including MongoDB, Apache Kafka, and Hadoop.

Step 2: Preparing the Data

Data scientists then move the raw data to the data preparation stage. You can use many tools to prepare your data science data, such as SQL, Python, Apache Spark, Pandas, and scikit-learn. While preparing data, including removing noise and outliers, data scientists use plenty of extract, transform, and load (ETL) processes. So, you need to seek ways to scale and tune the process.

Step 3: Training the Model

In this step, data scientists train a machine learning (ML) model to extract hidden patterns, trends, and relationships from their data. Model training commonly uses tools like R, Apache Spark, TensorFlow, PyTorch, and XGBoost. The training involves many hyperparameters. So, you need to think of how to tune these parameters while scaling the process.

Step 4: Deploying the Model

When you’ve finished training your model, you can deploy it into a production environment, using tools like Amazon SageMaker, Docker, and Apache Spark. Then you can use your model to perform tasks such as classification and prediction on new data.

The data science lifecycle is a vicious cycle: after receiving the raw data, you need to prepare it, train the model, then deploy it.

Data Engineering is Vital for Successful Data Science

Most of the data organizations collect from the environment or that systems generate aren’t ready for analysis. This data is dirty. Someone needs to take this data through many processes, including partitioning, aggregation, merging, and others to prepare it for analysis.

This cleaning is the work of data engineers. These team members clean data, organize it and get it ready for data science.

Data engineering plays two significant roles in ensuring successful data science projects. First, it helps data scientists develop, test, and maintain high-quality data pipelines. These pipelines can provide reliable data quickly, securely, and efficiently. Second, data engineering helps data scientists “productionize” data science models with robust and maintainable code.

Delta Lake Helps Prepare Data for Analytics

Data lakes provide a single repository to store all types of data, in various formats, from multiple sources. However, most organizations find it challenging to cope with their data lake’s extreme growth.

Some data scientists use the term “data swamp,” referring to data lakes without data lifecycle management, contextual metadata, curation, or data governance. Poor storage makes data hard to use or even unusable. The data may become unreliable or inconsistent, slowing analytics. Users may also take too long to find the information they need.

Various factors may turn a data lake into a data swamp, including:

  • Failed production jobs: These failed jobs leave data in a corrupt state, requiring tedious recovery procedures.
  • Too many small or large files: Extreme file sizes require more time to open and close. Data scientists could better spend this time reading data. Streaming data makes this file size issue even worse.
  • Lack of transactions: A lack of transactions makes it hard to mix reads and appends, batches, and streaming.
  • Lack of schema enforcement: Insufficient schema validation creates low-quality, inconsistent data.
  • Indexing or partitioning breakdown: When the data has high cardinality columns or many dimensions, indexing or partitioning breaks down.
  • A large number of files: Processing systems and storage engines find it difficult to handle a large number of files and subdirectories.

Delta Lake solves these problems while improving data lake data reliability with minimal changes to the data architecture. The software’s many features enable users to query vast volumes of data and facilitate accurate and reliable analytics.

These features include unified batch and streaming processing, ACID compliance, metadata, scalable storage, time travel (batch processing), and scalable storage. Delta Lake offers these features to solve data lakes’ significant challenges:

  • Reliability: Delta Lake doesn’t allow failed writes to update the commit logs. So, when there are corrupt or partial files, any Delta users using the Delta table won’t see the corrupted file.
  • Performance: Delta Lake compacts transactions using OPTIMIZE by applying multi-dimensional clustering on multiple columns.
  • Consistency: Delta tables store changes as ordered, atomic commits. Delta readers then read the logs in a consistent, atomic snapshot each time. Every commit comprises many actions filed in a directory. Practically, there are no conflicts between most writes and tunable isolation levels.
  • Reduced system complexity: Delta Lake can handle batch and streaming data (through integration with structured streaming). It can also write batch and streaming data concurrently to one table.

Adopting Delta Lake to Power Your Data Lake is Easy

Delta Lake has a simple, easy-to-implement architecture. It uses a continuous data flow model to unify batch and streaming data. The Delta Lake architecture follows these steps:

Step 1: Getting Raw Data

The architecture starts with raw data. This data is dirty and is full of noise and inconsistencies.

Step 2: Creating a Bronze Table

Once you’ve gathered all your raw data, you create a Bronze table to store it. This Bronze table is similar to your original data lake, as you dump your data there using inserts.

Step 3:  Building a Silver Table

Next, you build your Silver table to contain cleaned, filtered, and augmented data. This table allows you to clean your data and combine it with data from other sources. You perform many deletes here.

Step 4: Moving to a Gold Table

You then move your data to a Gold table. You perform your business-level aggregates, machine learning, artificial intelligence (AI), deep learning, data science, and more in the Gold table. Here, you do many merges and overwrites.

AI and reporting tools for visualization and analytics can then access the data. Each of the above steps progressively makes your data cleaner.

Next Steps

Organizations use data lakes to store structured, unstructured, historical, and transactional data. But with the huge volumes of data stored in data lakes, it’s easy to lose control of cataloging and indexing the data. So, the data may become inconsistent and unreliable.

Its makers developed Delta Lake to bring reliability to data lakes. As a storage layer sitting on top of a data lake, it facilitates high-quality data pipelines and improves data reliability. The cleaner, more reliable data makes data science operations more successful.

Delta Lake is open-source software, backed by a strong community and enterprise-level tools. Learn more and try Delta Lake to spend less time cleaning data and more time gaining data insights.

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