Big data storage and compatibility is still in its infancy, and it can be a hurdle for even the most experienced database administrator to understand. Moving from traditional SQL and relational databases to big data reservoirs is an exercise in patience. It’s also challenging for administrators used to standard database warehouse design. The move is even more challenging when you need to transform “dirty data” into organized data sets. With Apache Spark, much of the overhead and difficult processing is handled by its core, and developers can take advantage of its API to drive data science queries for advanced analysis and reports that traditional database engines don’t handle well.

Understanding Big Data in General

Before we get into Sparks and its functionality, you should first understand big data and how it differs from traditional structured data. Big data is a term given to storage of unclassified and unstructured data. You can import and work with structured data in a big data environment, but big data powers queries when it can’t be defined by traditional database design.

With structured data, you know how it must be stored. Take ecommerce ordering as an example. You know that you need a name, shipping address, payment information, and the product ordered. It’s structured, because the information is the same regardless of the customer. But what if you just want data from a web page, and there is no way to define its structure? This is where big data comes in handy.

Big data lets you retrieve and store data without knowing how it is categorized. You’ll need to scrub and clean the data in the future, but with data science analysis you just want as much data as you can retrieve. In the example of a web page, suppose you want to crawl the web for websites that match certain criteria. Every website and its internal pages are structured in their own way, so you just need to scrape and store data and organize it later. Big data lets you do it. With traditional relational databases, you need to organize the data as you scrape it, which can be a long process that takes months to optimize.

Big data architecture and patterns

ETL and Spark SQL

Traditional relational databases use Structured Query Language (SQL), but as we discussed, big data isn’t stored in a structured manner. These engines use NoSQL, which is much different than what you’ll see with relational databases. Apache Spark handles this change using a language called Spark SQL. It’s included with the Spark Core services, so you have it as long as you have Apache Spark.

Apache Spark is a form of NoSQL, which is the name given to the language used to query big data databases. Most engines that support big data have a form of NoSQL, so if you’re used to a different engine, you should be able to smoothly transition into the Apache Spark programming environment with little hassles. Just like traditional SQL languages (Oracle, MySQL, and SQL Server), there are slight variances with the language, but they are close enough to be easy to learn for someone with experience in at least one language.

Extract, transform, and load (ETL) processes are common in big data infrastructure. Remember that big data lets you store unstructured data, but you need a way to identify and define it for analysis. This is where ETL is used. ETL procedures take your unstructured data and move it to a more structured location. It could be the same database, but most enterprises move the data to either a NoSQL cloud database or even a relational one. You can even extract it to a flat CSV file if you’re transferring between platforms. What you do with the structured data depends on your analysis requirements and where you want to move it.

Spark SQL simplifies it by transforming data from one database to another. It helps the administrator scrub data, which is a term given to analyze and “clean” data from unwanted formats or remove characters and typos and store it in a more structured manner. For instance, suppose you have phone numbers in various formats – with parenthesis, the + symbol for country code, and hyphens to separate numbers. This can make it difficult to just parse out a phone number, so instead you can clean the data from special characters and store only numbers after the ETL process.

Building Analytics Reports in Sparks

One benefit of Sparks is its in-memory analytics. If you’re familiar with basic web caching, you know that storing anything in memory is far faster than retrieving it from another server or even on the hard drive. The in-memory analytics component of Sparks makes it an extremely fast way to work with analytics and building reports.

Nick Heudecker, a researcher at Gartner, reduced the speed of an ETL process from four hours to 90 seconds using Spark. Remember that the first step to analysis is first transforming data into something readable for analysis. By reducing the ETL process to under 2 minutes, you can run much more analysis during the day. Database procedures that take 4 hours are usually set aside as adhoc reports or nightly procedures that are only available to users with data from the previous 24 hours. Using Spark, you can give your users more real-time data for their reporting.

Sparks provides an API, so users can write analysis using almost any language. The Spark Core API supports R, SQL, Python, Scala and Java. With data science, most queries are run in R or Python, because of the data analysis functions available in the core libraries.

Machine Learning

Big data is the foundation for machine learning. Machine Learning takes the vast amount of data, analyzes it, and provides answers based on patterns. Spark includes the MLlib, so the machine learning core is already taken care of from a developer’s aspect.

KD Nuggets came up with a use case scenario in which Sparks was beneficial for developers and data scientists that need machine learning for their projects. Remember that Apache has in-memory processing, which makes it fast. Suppose that you use another engine for your machine learning, but your data doesn’t fit in the current server’s memory. In most scenarios, you’d run your machine learning procedures in a clustered environment where servers share resources for large data processing. You could use clustered Apache Spark servers, but you can process most large analysis procedures using just one standalone machine.

Apache Spark is 100 times faster than Hadoop for large-scale data processing. It’s made for data science and machine learning, which makes it useful for developers and enterprise organizations that have large reservoirs of data for analytics.

Sparks has some practical use cases for any enterprise that is unsure how to leverage big data. Just a few include:

Marketing and advertising: improve user engagement by building analytics from aggregated user data that spans buying patterns, specifications, and user-behavior data. Build better ROI habits from creating ads specifically to your user base.

Security monitoring and intrusion detection: use heuristics to quickly find suspicious traffic. This could be insider traffic that doesn’t fit standard patterns, or use a benchmark for file access to determine if suspicious activity levels are targeted at intellectual documentation or private user data.

Procedural optimization: productivity gets costly when it becomes disorganized and inefficient. Sparks and machine learning can identify areas of inefficiency and help data scientists make procedural recommendations in the supply chain.

If you’re ready to make the move to big data, machine learning, and better analytics, Taazaa can help.