Traditionally, data is ingested under strict rules and formatted according to a predetermined schema. This process is known as Extract, Transform, Load (ETL), and is still a very common practice supported by a large array of commercial tools as well as some open source products.

The ETL approach favors performing up-front checks, which ensure data quality and schema conformance, in order to simplify follow-on online analytical processing. It is particularly suited to handling data with a specific set of characteristics, namely, those that relate to a classical entity-relationship model. However, it is not suitable for all scenarios.

During the big data revolution, there was a metaphorical explosion of demand for structured, semi-structured, and unstructured data, leading to the creation of systems that were required to handle data with a different set of characteristics. These came to be defined by the phrase, 4 Vs: Volume, Variety, Velocity, and Veracity http://www.ibmbigdatahub.com/infographic/four-vs-big-data. While traditional ETL methods floundered under this new burden-because they simply required too much time to process the vast quantities of data, or were too rigid in the face of change, a different approach emerged. Enter the schema-on-read paradigm. Here, data is ingested in its original form (or at least very close to) and the details of normalization, validation, and so on are done at the time of analytical processing.

This is typically referred to as Extract Load Transform (ELT), a reference to the traditional approach:

This approach values the delivery of data in a timely fashion, delaying the detailed processing until it is absolutely required. In this way, a data scientist can gain access to the data immediately, searching for insight using a range of techniques not available with a traditional approach.

Although we only provide a high-level overview here, this approach is so important that throughout the book we will explore further by implementing various schema-on-read algorithms. We will assume the ELT method for data ingestion, that is to say we encourage the loading of data at the user's convenience. This may be every n minute, overnight or during times of low usage. The data can then be checked for integrity, quality, and so forth by running batch processing jobs offline, again at the user's discretion.

A data lake is a convenient, ubiquitous store of data. It is useful because it provides a number of key benefits, primarily:

Let's take a brief look at each of these.

A data science platform provides services and APIs that enable effective data science to take place, including explorative data analysis, machine learning model creation and refinement, image and audio processing, natural language processing, and text sentiment analysis.

This is the area where Spark really excels and forms the primary focus of the remainder of this book, exploiting a robust set of native machine learning libraries, unsurpassed parallel graph processing capabilities and a strong community. Spark provides truly scalable opportunities for data science.

The remaining chapters will provide insight into each of these areas, including Chapter 6, Scraping Link-Based External Data, Chapter 7, Building Communities, and Chapter 8, Building a Recommendation System.

Data in a data lake is most frequently accessed by data engineers and scientists using the Hadoop ecosystem tools, such as Apache Spark, Pig, Hive, Impala, or Drill. However, there are times when other users, or even other systems, need access to the data and the normal tools are either too technical or do not meet the demanding expectations of the user in terms of real-world latency.

In these circumstances, the data often needs to be copied into data marts or index stores so that it may be exposed to more traditional methods, such as a report or dashboard. This process, which typically involves creating indexes and restructuring data for low-latency access, is known as data egress.

Fortunately, Apache Spark has a wide variety of adapters and connectors into traditional databases, BI tools, and visualization and reporting software. Many of these will be introduced throughout the book.