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A Big Data Lake for Multilevel Streaming Analytics

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A Big Data Lake for Multilevel Streaming Analytics A Big Data Lake for Multilevel Streaming Analytics Ruoran Liu Haruna Isah Farhana Zulkernine School of Computing School of Computing School of Computing Queen’s University Queen’s University Queen’s University Kingston, Canada Kingston, Canada Kingston, Canada [email protected] [email protected] [email protected] Abstract—Large organizations are seeking to create new security, or authorization [3]. It is a large storage repository that architectures and scalable platforms to effectively handle data holds a vast amount of raw data in its native format until it is management challenges due to the explosive nature of data rarely needed. This is in contrast to the traditional data warehouse seen in the past. These data management challenges are largely approach, also known as schema on write, which requires more posed by the availability of streaming data at high velocity from upfront design and assumptions about how the data will be used. various sources in multiple formats. The changes in data paradigm In a data warehouse, adding data to a database requires data to have led to the emergence of new data analytics and management be transformed into a pre-determined schema before it can be architecture. This paper focuses on storing high volume, velocity loaded. This step often consumes a lot of time, effort, and and variety data in the raw formats in a data storage architecture expense before the data can be used for downstream applications called a data lake. First, we present our study on the limitations of [4]. A data lake is meant to complement and not replace a data traditional data warehouses in handling recent changes in data paradigms. We discuss and compare different open source and warehouse. At the core of a data lake is a set of repositories and commercial platforms that can be used to develop a data lake. We components such as the traditional Relational Database then describe our end-to-end data lake design and implementation Management System (RDBM) warehouses, operational data approach using the Hadoop Distributed File System (HDFS) on hubs, and distributed file system clusters [5]. the Hadoop Data Platform (HDP). Finally, we present a real-world There are several reasons why organizations are now data lake development use case for data stream ingestion, staging, considering the deployment of a data lake, which include the and multilevel streaming analytics which combines structured and need to (i) define an infrastructure to manage repositories for all unstructured data. This study can serve as a guide for individuals types of data i.e., structured, semi-structured or unstructured or organizations planning to implement a data lake solution for data with lineage and appropriate governance that will enable their use cases. the organization to be compliant; (ii) provide a means for storing documents for specific analytical studies and reuse by multiple Keywords—Hadoop Data Platform, Hadoop Distributed File users including business users and data scientists; (iii) provide System, NiFi, streaming data, unstructured data the necessary data lineage back to source systems, and (iv) enable users to access the analytical contents in a self-serve I. INTRODUCTION manner. For data science purposes, keeping all the data in a raw Big data is often described based on its five main aspects format is beneficial since often it is not clear upfront which data which include variety, velocity, volume, value and veracity. items may be valuable for a certain data analytics goal [5]. According to Mohamed et al. [1], variety refers to the different A data lake can either be designed from scratch or developed forms of big data types and sources such as structured, semi- on top of existing platforms. An example of a data lake design structured and unstructured data generated on social media, from the scratch is Constance, a unified interface and query Internet of Things (IoT), and smart-phones; velocity refers to the processing system by Hai et al. [4] for managing structural and speed and frequency of the data generation; volume refers to the semantic metadata, enriching metadata with schema, and size of data being generated in gigabytes, terabytes and matching and summarizing schema. An example of a data lake petabytes quantity; veracity refers to the extent data can be development on top of existing platforms is CUTLER, a system trusted, given the reliability of its source; and finally, value built on the Hadoop platform to gather data from a variety of corresponds to variation in data values. Big data is playing a sources for smart city analytics by Mehmood et al. [6]. Cloud critical role in many application areas including healthcare, platforms provide services that can be weaved together in an cybersecurity, business, and marketing. Big data analytics economical way to achieve the scalability requirement of a data outcomes can help companies improve operations and uncover lake. Examples of platforms that can be used to develop data insights [2]. However, as data continues to flow from disparate lake include Amazon Web Services (AWS)1, Microsoft Azure2, sources, it is important to channel and maintain it in a central Google Cloud 3 , IBM Cloud 4 , and Hadoop ecosystems repository to enable effective data handling and analytics. (Cloudera, Hortonworks, and MapR) [7]. A data lake is an enterprise data management solution for One of the main challenges of building a data lake is the storing vast amounts of data by bringing all the data into a single understanding of the purpose of the system, analytics needs, and repository for further analysis without the restrictions of schema, limitations of the possible components or technology that can be 1 https://aws.amazon.com/ 3 https://cloud.google.com/ 2 https://azure.microsoft.com/ 4 https://www.ibm.com/cloud XXX-X-XXXX-XXXX-X/XX/$XX.00 ©20XX IEEE used to create the data lake [8]. Our industry-academic The flow and processing of data from various sources collaboration with a media monitoring and analytics company including social media and IoT are shown in Fig. 1 to illustrate that serves clients from various sectors, focuses on developing the difference between a data warehouse and a data lake. The an efficient and scalable data analytics and management ETL process places the data in a schema as it stores (writes) the framework, which can facilitate complex multilevel predictive data to the relational database whereas the data lake stores the analytics for real-time streaming data from a variety of sources data in raw form. When any of the Hadoop applications use the including the Web and social media. This requires a data lake data, the schema is applied to data as they are read from the data for the storage, analyses and query of high-velocity data. lake. The ETL step often discards some data as part of the We began with studying the existing technology options for process. In both the warehouse and Hadoop data lake cases, developing the data lake. Following a thorough literature users access the data they need. However, in the case of Hadoop, review, we implemented a data lake on top of the Hadoop it can happen as soon as the data are available in the data lake. Distributed File System (HDFS) to reliably store very large files across multiple clusters [9]. HDFS enables the use of the open- source Hadoop stack and does not enforce on adding a schema to the data flowing into the data lake [8]. The rest of this paper is organized as follows. Section II presents a background study on data warehouse, data lake, and platforms for data lake development. An overview of our Hadoop-based data lake is depicted in Section III. Section IV provides the data lake implementation details. The experimental and evaluation results are presented in Section V. Finally, Section VI presents a conclusion and a list of future work. II. BACKGROUND The traditional data warehouse technology is based on the Extract, Transform and Load (ETL) data processing stages, which are no longer suitable for the extremely high rate at which data are emanating today from various sources [10]. The data lake has emerged as a tool that enables organizations to define, manage and govern the use of various big data technologies. The Fig. 1. Data warehouse versus data lake. differences between a data warehouse and a data lake are outlined in TABLE 15. As mentioned in the introductory section, a data warehouse is a valuable business tool still in use in many data analytics TABLE I. DATA WAREHOUSE VS DATA LAKE pipelines. Data lake was developed to complement the data warehouse. The basic idea of a data lake is simple, all data Features Data Warehouse Data Lake collected by an organization will be stored in a single data Data Data is processed, only Data is raw, all data types repository in the lake in their original format. As such, the structure structured information is (structured, semi- complex preprocessing and transformation of loading data into captured and organized in structured, unstructured) is schemas captured in its original a warehouse is eliminated. Also, the upfront costs of data form ingestion are reduced [11]. The major benefit of a data lake is Users Ideal for operational Ideal for advanced users the centralization of content from disparate sources. A data lake users such as business such as data scientists who can have tens of thousands of tables or files and billions of analyst since the data is carry out deep analysis records, which demands for scalable data storage, management structured and easy to use with advanced analytical and analytics. Once gathered at the data lake, data from multiple tools sources can be correlated, integrated and processed using Storage Storing data is time- Storing data is relatively leading edge big data analytics and search techniques which costs consuming and costly inexpensive would have otherwise been impossible.
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