Storage and Analysis of Big Data Tools for Sessionized Data

The Oracle database currently used to mine data at PEGGY is approaching end-of-life and a new infrastructure overhaul is required. It has also been identified that a critical business requirement is the need to load and store very large historical data sets. These data sets contain raw electronic consumer events and interactions from a website such as page views, clicks, downloads, return visits, length of time spent on pages, and how they got to the site / originated. This project will be focused on finding a tool to analyze and measure sessionized data, which is a unit of measurement in web analytics that captures either a user\u27s actions within a particular time period, or the process of segmenting user activity of each user into sessions, each representing a single visit to the site. This sessionized data can be used as the input for a variety of data mining tasks such as clustering, association rule mining, sequence mining etc (Ansari. 2011) This sessionized data must be delivered in a reorganized and readable format timely enough to make informed go-to-market decisions as it relates to the current and existing industry trends. It is also pertinent to understand any development work required and the burden on the resources. Legacy on-premise data warehouse solutions are becoming more expensive, less efficient, less dynamic, and unscalable when compared to current Cloud Infrastructure as a Service (IaaS) that offer real time, on-demand, pay-as-you-go solutions . Therefore, this study will examine the total cost of ownership (TCO) by considering, researching, and analyzing the following factors against a system wide upgrade of the current on-premise Oracle Real Application Cluster (RAC) System: High performance: real-time (or as close to as possible) query speed against sessionized data SQL compliance Cloud based or, at least a hybrid (read: on-premise paired with cloud) Security: encryption preferred Cost structure: cost-effective pay-as-you-go pricing model and resources required for the migration and operations. These technologies analyzed against the current Oracle database are: Amazon Redshift Google Bigquery Hadoop Hadoop + Hive The cost of building an on-premise data warehouse is substantial. The project will determine the performance capabilities and affordability of Amazon Redshift, when compared to other emerging highly ranked solutions, for running e-commerce standard analytics queries on terabytes of sessionized data. Rather than redesigning, upgrading, or over purchasing infrastructure at a high cost for an on-premise data warehouse, this project considers data warehousing solutions through cloud based infrastructure as a service (IaaS) solutions. The proposed objective of this project is to determine the most cost-effective high performer between Amazon Redshift, Apache Hadoop, and Google BigQuery when running e-commerce standard analytics queries on terabytes of sessionized data

Digital content popularity counting with Amazon Web Services

The page hit counter system processes, counts and stores page hit counts gathered from page hit events from a news media company’s websites and mobile applications. The system serves a public application interface which can be queried over the internet for page hit count information. In this thesis I will describe the process of replacing a legacy page hit counter system with a modern implementation in the Amazon Web Services ecosystem utilizing serverless technologies. The process includes the background information, the project requirements, the design and comparison of different options, the implementation details and the results. Finally, I will show how the new system implemented with Amazon Kinesis, AWS Lambda and Amazon DynamoDB has running costs that are less than half of that of the old one’s

Hive on spark and MapReduce : a methodology for parameter tuning

Project Work presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Information Systems and Technologies ManagementAs the era of “big data” has arrived, more and more companies start using distributed file systems to manage and process their data streams like the Hadoop distributed file system framework (HDFS). This software library offers a way to store large files across multiple machines. Large data sets are processed by using its inherent programming model MapReduce. Apache Spark is a relatively new alternative to Hadoop MapReduce and claims to offer a performance boost up to 10 times for certain applications, while maintaining its automatic fault tolerance. To leverage the Data Warehouse capabilities of Hadoop Apache Hive was introduced. It is a concept for Big Data analytics that works on top of Hadoop and provides data analysis tools and most importantly translates queries to MapReduce and Spark jobs. Therefore, it exploits the scalability of Hadoop and offers data exploration and mining capabilities to non-developers. However, it is difficult for users to utilize the full potential of the Apache Spark execution engine. This results in very long execution times. Therefore, this project work gives researches and companies a tuning methodology that significantly can improve the execution time of queries. As a result, this tuning methodology could optimize a real-world batch-processing query by 5 times. Moreover, it gives insides in the underlying reasons of this big improvement by using Apache Spark Monitoring tools. The result can be helpful for many practitioners and researchers that would like to optimise the performance of Spark and MapReduce queries executed in Hive on top of an Apache Hadoop cluster

Deep Lake: a Lakehouse for Deep Learning

Traditional data lakes provide critical data infrastructure for analytical workloads by enabling time travel, running SQL queries, ingesting data with ACID transactions, and visualizing petabyte-scale datasets on cloud storage. They allow organizations to break down data silos, unlock data-driven decision-making, improve operational efficiency, and reduce costs. However, as deep learning takes over common analytical workflows, traditional data lakes become less useful for applications such as natural language processing (NLP), audio processing, computer vision, and applications involving non-tabular datasets. This paper presents Deep Lake, an open-source lakehouse for deep learning applications developed at Activeloop. Deep Lake maintains the benefits of a vanilla data lake with one key difference: it stores complex data, such as images, videos, annotations, as well as tabular data, in the form of tensors and rapidly streams the data over the network to (a) Tensor Query Language, (b) in-browser visualization engine, or (c) deep learning frameworks without sacrificing GPU utilization. Datasets stored in Deep Lake can be accessed from PyTorch, TensorFlow, JAX, and integrate with numerous MLOps tools

Data Warehousing in the Cloud

Um data warehouse, mais que um conceito, é um sistema concebido para armazenar a informação relacionada com as atividades de uma organização de forma consolidada e que sirva de ponto único para toda e qualquer relatório ou análise que possa ser efetuada. Este sistema possibilita a análise de grandes volumes de informação que tipicamente têm origem nos sistemas transacionais de uma organização (OLTP – Online Transaction Processing). Este conceito surgiu da necessidade de integrar dados corporativos espalhados pelos vários servidores aplicacionais que uma organização possa ter, para que fosse possível tornar os dados acessíveis a todos os utilizadores que necessitam de consumir informação e tomar decisões com base nela. Com o surgimento de cada vez mais dados, surgiu também a necessidade de os analisar. No entanto os sistemas de data warehouse atuais não têm a capacidade suficiente para o tratamento da quantidade enorme de dados que atualmente é produzida e que necessita de ser tratada e analisada. Surge então o conceito de cloud computing. Cloud computing é um modelo que permite o acesso ubíquo e a pedido, através da Internet, a um conjunto de recursos de computação partilhados ou não (tais como redes, servidores ou armazenamento) que podem ser rapidamente aprovisionados ou libertados apenas com um simples pedido e sem intervenção humana para disponibilizar/libertar. Neste modelo, os recursos são praticamente ilimitados e em funcionamento conjunto debitam um poder de computação muito elevado que pode e deve ser utilizado para os mais variados fins. Da conjugação de ambos estes conceitos, surge o cloud data warehouse que eleva a forma como os sistemas tradicionais de data warehouse são definidos ao permitir que as suas fontes possam estar localizada em qualquer lugar desde que acessível pela Internet, tirando também partido do grande poder computacional de uma infraestrutura na nuvem. Apesar das vantagens reconhecidas, há ainda alguns desafios sendo dois dos mais sonantes a segurança e a forma como os dados são transferidos para a nuvem. Nesta dissertação foi feito um estudo comparativo entre variadas soluções de data warehouse na cloud com o objectivo de recomendar a melhor solução de entre as estudadas e alvo de testes. Foi feita uma avaliação com base em critérios da Gartner e num inquérito sobre o tema. Desta primeira avaliação surgiram as duas soluções que foram alvo de uma comparação mais fina e sobre as quais foram feitos os testes cuja avaliação ditou a recomendação.A data warehouse, rather than a concept, is a system designed to store the information related to the activities of an organization in a consolidated way and that serves as a single point of truth for any report or analysis that can be carried out. It enables the analysis of large amounts of information that typically comes from the organization's transactional systems (OLTP). This concept arose from the need to integrate corporate data across multiple application servers that an organization might have, so that it would be possible to make data accessible to all users who need to consume information and make decisions based on it. With the appearance of more and more data, there has also been a need to analyze it. However, today's data warehouse systems do not have the capacity to handle the huge amount of data that is currently produced and needs to be handled or analyzed. Then comes the concept of cloud computing. Cloud computing is a model that enables ubiquitous and on-demand access to a set of shared or non-shared computing resources (such as networks, servers, or storage) that can be quickly provisioned or released only with a simple request and without human intervention to get it done. In this model, the features are almost unlimited and in working together they bring a very high computing power that can and should be used for the most varied purposes. From the combination of both these concepts, emerges the cloud data warehouse. It elevates the way traditional data warehouse systems are defined by allowing their sources to be located anywhere as long as it is accessible through the Internet, also taking advantage of the great computational power of an infrastructure in the cloud. Despite the recognized advantages, there are still some challenges. Two of the most important are the security and the way data is transferred to the cloud. In this dissertation a comparative study between several data warehouse solutions in the cloud was carried out with the aim of recommending the best solution among the studied solutions. An assessment was made based on Gartner criteria and a survey on the subject. From this first evaluation came the two solutions that were the target of a finer comparison and on which the tests whose assessment dictated the recommendation were made

Evaluation and performance of reading from big data formats

The emergence of new application profiles has caused a steep surge in the volume of data generated nowadays. Data heterogeneity is a modern trend, as unstructured types of data, such as videos and images, and semi-structured types, such as JSON and XML files, are becoming increasingly widespread. Consequently, new challenges related to analyzing and extracting important insights from huge bodies of information arise. The field of big data analytics has been developed to address these issues. Performance plays a key role in analytical scenarios, as it empowers applications to generate value in a more efficient and less time-consuming way. In this context, files are used to persist large quantities of information, which can be accessed later by analytic queries. Text files have the advantage of providing an easier interaction with the end user, whereas binary files propose structures that enhance data access. Among them, Apache ORC and Apache Parquet are formats that present characteristics such as column-oriented organization and data compression, which are used to achieve a better performance in queries. The objective of this project is to assess the usage of such files by SAP Vora, a distributed database management system, in order to draw out processing techniques used in big data analytics scenarios, and apply them to improve the performance of queries executed upon CSV files in Vora. Two techniques were employed to achieve such goal: file pruning, which allows Vora’s relational engine to ignore files possessing irrelevant information for the query, and block pruning, which disregards individual file blocks that do not possess data targeted by the query when processing files. Results demonstrate that these modifications enhance the efficiency of analytical workloads executed upon CSV files in Vora, thus narrowing the performance gap of queries executed upon this format and those targeting files tailored for big data scenarios, such as Apache Parquet and Apache ORC. The project was developed during an internship at SAP, in Walldorf, Germany.A emergência de novos perfis de aplicação ocasionou um aumento abrupto no volume de dados gerado na atualidade. A heterogeneidade de tipos de dados é uma nova tendência: encontram-se tipos não-estruturados, como vídeos e imagens, e semi-estruturados, tais quais arquivos JSON e XML. Consequentemente, novos desafios relacionados à extração de valores importantes de corpos de dados surgiram. Para este propósito, criou-se o ramo de big data analytics. Nele, a performance é um fator primordial pois garante análises rápidas e uma geração de valores eficiente. Neste contexto, arquivos são utilizados para persistir grandes quantidades de informações, que podem ser utilizadas posteriormente em consultas analíticas. Arquivos de texto têm a vantagem de proporcionar uma fácil interação com o usuário final, ao passo que arquivos binários propõem estruturas que melhoram o acesso aos dados. Dentre estes, o Apache ORC e o Apache Parquet são formatos que apresentam uma organização orientada a colunas e compressão de dados, o que permite aumentar o desempenho de acesso. O objetivo deste projeto é avaliar o uso desses arquivos na plataforma SAP Vora, um sistema de gestão de base de dados distribuído, com o intuito de otimizar a performance de consultas sobre arquivos CSV, de tipo texto, em cenários de big data analytics. Duas técnicas foram empregadas para este fim: file pruning, a qual permite que arquivos possuindo informações desnecessárias para consulta sejam ignorados, e block pruning, que permite eliminar blocos individuais do arquivo que não fornecerão dados relevantes para consultas. Os resultados indicam que essas modificações melhoram o desempenho de cargas de trabalho analíticas sobre o formato CSV na plataforma Vora, diminuindo a discrepância de performance entre consultas sobre esses arquivos e aquelas feitas sobre outros formatos especializados para cenários de big data, como o Apache Parquet e o Apache ORC. Este projeto foi desenvolvido durante um estágio realizado na SAP em Walldorf, na Alemanha

A Business Intelligence Solution, based on a Big Data Architecture, for processing and analyzing the World Bank data

The rapid growth in data volume and complexity has needed the adoption of advanced technologies to extract valuable insights for decision-making. This project aims to address this need by developing a comprehensive framework that combines Big Data processing, analytics, and visualization techniques to enable effective analysis of World Bank data. The problem addressed in this study is the need for a scalable and efficient Business Intelligence solution that can handle the vast amounts of data generated by the World Bank. Therefore, a Big Data architecture is implemented on a real use case for the International Bank of Reconstruction and Development. The findings of this project demonstrate the effectiveness of the proposed solution. Through the integration of Apache Spark and Apache Hive, data is processed using Extract, Transform and Load techniques, allowing for efficient data preparation. The use of Apache Kylin enables the construction of a multidimensional model, facilitating fast and interactive queries on the data. Moreover, data visualization techniques are employed to create intuitive and informative visual representations of the analysed data. The key conclusions drawn from this project highlight the advantages of a Big Data-driven Business Intelligence solution in processing and analysing World Bank data. The implemented framework showcases improved scalability, performance, and flexibility compared to traditional approaches. In conclusion, this bachelor thesis presents a Business Intelligence solution based on a Big Data architecture for processing and analysing the World Bank data. The project findings emphasize the importance of scalable and efficient data processing techniques, multidimensional modelling, and data visualization for deriving valuable insights. The application of these techniques contributes to the field by demonstrating the potential of Big Data Business Intelligence solutions in addressing the challenges associated with large-scale data analysis

Benchmarking Apache Arrow Flight -- A wire-speed protocol for data transfer, querying and microservices

Moving structured data between different big data frameworks and/or data warehouses/storage systems often cause significant overhead. Most of the time more than 80\% of the total time spent in accessing data is elapsed in serialization/de-serialization step. Columnar data formats are gaining popularity in both analytics and transactional databases. Apache Arrow, a unified columnar in-memory data format promises to provide efficient data storage, access, manipulation and transport. In addition, with the introduction of the Arrow Flight communication capabilities, which is built on top of gRPC, Arrow enables high performance data transfer over TCP networks. Arrow Flight allows parallel Arrow RecordBatch transfer over networks in a platform and language-independent way, and offers high performance, parallelism and security based on open-source standards. In this paper, we bring together some recently implemented use cases of Arrow Flight with their benchmarking results. These use cases include bulk Arrow data transfer, querying subsystems and Flight as a microservice integration into different frameworks to show the throughput and scalability results of this protocol. We show that Flight is able to achieve up to 6000 MB/s and 4800 MB/s throughput for DoGet() and DoPut() operations respectively. On Mellanox ConnectX-3 or Connect-IB interconnect nodes Flight can utilize upto 95\% of the total available bandwidth. Flight is scalable and can use upto half of the available system cores efficiently for a bidirectional communication. For query systems like Dremio, Flight is order of magnitude faster than ODBC and turbodbc protocols. Arrow Flight based implementation on Dremio performs 20x and 30x better as compared to turbodbc and ODBC connections respectively