Spark Data Engineering

In today’s world, data is present every step of the way. Technological innovations and advancements over the years have made a great impact on the vitality of data. These innovations include cloud technology, open-source projects, and the growth of data in scale.

When it comes to organizing this enormous volume of data, not only to make it comprehensive but also coherent, data engineers must step in.

Understanding Data Engineering

The primary role of a data engineer is to build data pipelines in order to enable data-driven decision-making. The data pipeline is tasked to get the company’s raw data into a place and format where it can be useful, such as in an analytics dashboard or a machine learning model.

In data engineering, the concept of a data lake is a key concept, which brings together data from across the organization into a single location. This data may come from a spreadsheet or a relational database from various departments and then the raw data is stored in a data lake.

However, use cases such as data exploration, Interactive Analytics, and Machine Learning require that the raw data be processed to create use-case-driven trusted datasets. For Data Exploration and Machine Learning use cases, users continually refine data sets for their analysis needs. As a result, every data lake implementation must enable users to iterate between data engineering and use cases such as interactive analytics and Machine Learning. This is called “Continuous Data Engineering.”

Continuous data engineering involves the interactive ability to author, monitor, and debug data pipelines. In a data lake, these pipelines are authored using standard interfaces and open-source frameworks such as SQL, Python, Apache Spark, and Apache Hive.

Data engineering plays an important role in allowing businesses to optimize data toward usability. It can be deployed in the following pursuits:

• Searching for the best practices for refining your software development life cycle
• Securing information and protecting your business from cyberattacks
• Increasing your business domain knowledge
• Bringing data together into one place via data integration tools

Stages of the Data Engineering Lifecycle

The data engineering lifecycle consists of many key stages that data engineers follow to transform raw data into a format that can be easily analyzed to derive insights. These stages include:

1. Data Acquisition: In this stage, the required data is identified and acquired from various sources such as databases, APIs, web scraping, or sensors. Data engineers must ensure that this data is relevant, accurate, and complete.
2. Data Cleaning: After acquiring the data, it needs to be cleaned, pre-processed, and transformed into a usable format. This stage is crucial for ensuring data quality and consistency by removing duplicates, filling in missing values, converting data types, and scaling data.
3. Data Transformation: In this stage, the data collected is converted into a format suitable for the desired analysis. This stage involves aggregating, summarizing, filtering, or combining data from multiple sources.
4. Data Storage: Once the data is transformed, it needs to be stored in a way that is accessible and secure. Data Engineers can choose to store the data in a relational database, a data warehouse, or a data lake, ensuring that the data is stored efficiently and can be easily queried and analyzed.
5. Data Analysis: This is the final stage that involves data analysis to extract insights and make data-driven decisions using statistical models, machine learning algorithms, or data visualization tools to identify patterns, trends, and correlations in the data.

By following a well-defined process, data engineers can optimize their workflow, enhance data quality, and ultimately drive better business outcomes.

Apache Spark vs Hadoop

Apache Spark is an open-source cluster computing framework that is used for fast and flexible large-scale data analysis.  It has grown to become one of the largest open source communities in big data with over 200 contributors from more than 50 organizations. Because Spark is quickly experiencing enterprise adoption, Qubole is delivering Apache Spark as a Service to make this framework easy and fast to deploy.

Spark is built on top of the Hadoop Distributed File System.

However, instead of using Hadoop MapReduce, it relies on its own parallel data processing framework which starts by placing data in Resilient Distributed Datasets (RDDs), a distributed memory abstraction that performs calculations on large Spark clusters in a fault-tolerant manner. Since the data is persisted in memory (and disc if it’s needed), Apache Spark can be significantly faster and more flexible than Hadoop MapReduce jobs for certain applications. Moreover, Apache Spark projects also add flexibility to its speed by offering APIs that allow developers to write queries in Java, Python or Scala.

Advantages of Spark:

1. Ideal for interactive processing, iterative processing, and event stream processing
2. Flexible, speedy, and powerful
3. Supports sophisticated analytics
4. Executes batch processing jobs faster than Hadoop MapReduce
5. Runs on Hadoop alongside other tools in the Hadoop ecosystem

Key differences between Apache Hive and Apache Spark

Data Preparation in Machine Learning

Data engineers are tasked to gather all the data they need and make it available in a format that’s computer-readable and understandable. This process of discovery creates the knowledge needed to understand more complex relationships, what matters and what doesn’t, and how to tailor the data preparation approach necessary to lay the groundwork for a great ML model.

Data preparation consists of several steps that consume more time than other aspects of machine learning application development. Since it is a time-intensive process, data engineers must pay attention to various considerations while preparing data for machine learning.

 Here are six key steps that need to be taken into consideration:

1. Problem formulation: In machine learning, data preparation is a lot more than just cleaning and structuring data. Therefore, data engineers must develop a detailed understanding of the problem to inform what you do and how you do it. This simple step is often skipped, even though it can make a significant difference in deciding what data to capture. It can also provide useful guidance on how the data should be transformed and prepared for the machine learning model.
2. Data collection and discovery: After identifying the machine learning problem to be solved, data engineers need to inventory potential data sources within an organization as well as third parties. The data collection process must consider not only what the data is representing, but also understand why it was collected and what it might mean, particularly when used in a different context.
3. Data exploration: In order to get meaningful insights, data engineers need to fully understand the data they’re working with. Data exploration means reviewing such things as the type and distribution of data contained within each variable, the relationships between variables, and how they vary relative to the outcome you’re predicting or interested in achieving. Data engineers can easily see trends and explore the data correctly by creating suitable visualizations before drawing conclusions.
4. Data cleansing and validation: Various data cleansing and validation techniques can help analytics teams identify and rectify inconsistencies, outliers, anomalies, missing data, and other issues. A wide range of tools can be used to cleanse and validate data for machine learning, ensuring good quality data.
5. Data structuring: Once data engineers are satisfied with their data, they need to consider the machine learning algorithms that are being used. Data Preprocessing tricks, such as data binning and smoothing can reduce a machine learning model’s variance by preventing it from being misled by minor statistical fluctuations in a data set.Other actions that data scientists often take in structuring data for machine learning include the following:
   • Data reduction, through techniques such as attribute or record sampling and data aggregation
   • Data normalization, which includes dimensionality reduction and data rescaling
   • Creating separate data sets for training and testing machine learning models.
6. Feature engineering and selection

The last stage in data preparation before developing a machine learning model is feature engineering and feature selection, which involves adding or creating new variables to improve a model’s output. Data engineers must address feature selection by choosing relevant features to analyze. Methods such as lasso regression and automatic relevance determination can help with feature selection.

Common Spark Use Cases in Data Engineering

1. Batch Processing: Spark is frequently used for batch processing of huge datasets as it reads data from multiple data sources, performs data transformations, and writes the results to a target data storage in this use case. The batch-processing features of Spark make it ideal for jobs like ETL (Extract, Transform, Load), data warehousing, and data analytics.
2. Real-time Data Streaming: Spark collects data from real-time data sources and performs real-time processing on the data stream.
3. Interactive Analytics: Among Spark’s most notable features is its capability for interactive analytics. By combining Spark with visualization tools, complex data sets can be processed and visualized interactively.
4. Fog Computing: With key stack components such as Spark Streaming, an interactive real-time query tool (Shark), a machine learning library (MLib), and a graph analysis engine (GraphX), Spark more than qualifies as a fog computing solution. In fact, many industry experts predict that Spark has the potential to emerge as the de facto fog infrastructure.
5. Machine learning: Spark’s ability to store data in memory and rapidly run repeated queries makes it a good choice for training machine learning algorithms. Spark’s scalable Machine Learning Library (MLlib) can work in areas such as clustering, classification, and dimensionality reduction, among many others. All this enables Spark to be used for some very common big data functions, like predictive intelligence, customer segmentation for marketing purposes, and sentiment analysis

Why Apache Spark as a Service From Qubole?

Apache Spark is a high-performance, distributed data processing engine that has become a widely adopted framework for machine learning, stream processing, batch processing, ETL, complex analytics, and other big data projects. The technology allows teams to have quick and efficient access to analyze structured, semi-structured, and unstructured data; with various interfaces to explore, collaborate, and work in a centralized environment.

Apache Spark is a versatile tool for data engineers to deal with their data processing needs across environments, especially complex ones. With its ability to cover a broad range of use cases all within one tool, it has become a widely adopted industry standard.

Spark provides a core data processing engine that has additional specialized libraries on top and flexible integrations into different languages, storage systems, and cluster managers which ultimately provides data engineers with a critical tool for managing, transacting, and ensuring the validity of the data in their environments.

Understanding the value of Apache Spark projects in Big Data analytics, Qubole’s goal is to deliver the power of Spark to both technical and business Hadoop users.  Qubole is offering Spark as a Service to make it easy to process and query data stored in Hive, HDFS, HBase, and Amazon S3.  With this service, we have integrated Spark into our Qubole Data Service (QDS) platform, allowing users to launch and provision Spark clusters and start running queries within minutes. Importantly, in the future, any number of data sources can be accessed and their data can easily be combined with Spark.

Along with ease of use, Spark as a Service also reduces the cloud-compute cost of running Spark on AWS using self-service auto-scaling to scale capacity up and down as needed.