Meta-Managed Data Exploration Framework and Architecture
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1 Meta-Managed Data Exploration Framework and Architecture
2 CONTENTS Executive Summary Meta-Managed Data Exploration Framework Meta-Managed Data Exploration Architecture Data Exploration Process: Modules And Flow Our Experience Conclusion References
3 Executive Summary The geometric increase in the volume (amount), velocity (speed) and variety (range) of data today has facilitated the evolution of new information architectures and tools which not only collect and store data, but use it (data) to create business value and provide innovative business insights. In fact, they have made harnessing the data to generate near real-time insights entirely possible. Data exploration was, traditionally, a field with a limited number of expert users. But it has come a long way since then, and is now a part and parcel of our daily work. So much so that through the democratization of information, we are now placing data in the hands of many while simultaneously keeping it a separate process. Having said so, the need to create insights on the fly instead of having to go through standard IT processes is on the rise. A meta-managed architecture has the necessary potential to enable this capability since it provides a blueprint for the lineage, storage, data structures and the functionality inherent to an enterprise-scale application. Metadata management can extend both application scope and functionality with minimal to no additional software development. Whenever actual software development is required to enhance the platform and resolve some unknown complexity, it is made possible by extending additional metadata sets without facing too many challenges. Unaware of these advances in data-related technologies, analysts spend an inordinate amount of time in data aggregation and interpretation. This process of data exploration is often the most tedious and time consuming aspect of the analysis. This paper explores how a meta-managed information architecture framework has the potential to track different data sources which are ingested into an information hub through well-defined data lineage and additional capabilities. These capabilities include interactive data curation, transformation, publication and collaboration. They also help data analysts and agents in focusing their time and energy on making informed decisions using reliable data sets instead of non-productive activities. Capabilities for exchanging data to downstream / upstream systems in near real time Data APIs for integration with internal / external systems. Data exploration features Exploratory queries - Search capabilities enabled Analytical query processing 5 Query Balanced 6 Information Xchange Next Generation Information Architecture 1 Meta Managed Governed 2 Ability to track data sources ingested into the information hub Track data lineage and provenance of storage and processing activities Centralized and coordinated management of projects / activities, managing change and modern data management technologies used for cleansing, standardizing and integrating the data leveraging a scalable computing platform Stream processor integrated with Analytics store to respond to queries for both real-time and historical data High volume data ingestion capabilities across multiple data formats Batch and Stream Oriented 4 Operability 3 Proper monitoring is critical to run a large-scale distributed cluster Near real-time operational dashboard to depict health of operations, like delivery effectiveness, sorting effectiveness, etc Figure 1: Six core strategic dimensions to build a strong future ready information architecture 03
4 Meta-Managed Data Exploration Framework Figure 2 Data Exploration and Visual Analysis Framework Agile data wrangling capability enables deep analysis of data sets and the derivation of breadth through insights. The data exploration workflow starts with one or more initial raw analytical data sets. Figure 2 describes a typical data wrangling framework. The salient characteristics of a data wrangling framework are: An overlap between the data exploration and analysis phases An integrated toolset with data exploration and visual analysis tools Involves multiple iterations of involved steps (like a typical data exploration process) Immediate revelation of data due to data exploration taking place before loading the data onto visualization and analysis tools Enables exploration at every stage of the analysis process as users discover interesting insights from raw or new data (as and when it becomes available) As a result, the data which evolves from the raw to the usable stage through the data wrangling analytical process leads to new and better business insights. Industries with active adoption Some departments in which the adoption of a meta-managed data exploration platform can deliver deeper business insights are: Life Sciences research is an area in which the potential of data analysis can be demonstrated conspicuously. The problem is that physicians, biologists and life scientists lack the required data analysis skills. Using a meta-managed data exploration platform can help overcome these and associated challenges. Some key benefits include accelerating clinical trials, facilitating drug innovation, identifying adverse drug reactions, as well as optimizing supply chain management and marketing. Financial services and Insurance institutions can leverage data exploration platforms to detect fraud. These platforms can also help identify and manage risks, assess portfolio valuations, build global customer 360 degree view while ensuring regulatory compliance, and improve investment management capabilities. CPG and Retail institutions can derive valuable insights from diverse data sets sourced from different suppliers, production facilities, logistics, partners and retailers. These institutions can use these platforms to improve their bottom lines by quickly responding to sales trends, thereby reducing costs. 04
5 Meta-Managed Data Exploration Architecture Figure 3 Meta-Managed Data Exploration Platform Architecture A brief description of the components as described in Figure 3 is provided in the table below: Components Data sources and Ingestion Data Transformation Meta Data Management, Data Security and Data Governance Data Exploration Technology and Tools Description Consists of enterprise internal and external data sources feeding input data sets to data exploration platform Comprises of various interactive data preparation service modules used by end users / data analysts to align, harmonize, integrate and enrich input data sets as part of the data exploration process. This layer also provides interfaces like Web Services or APIs, which are used to interact with Big Data distribution and traditional data repository components Encompasses capabilities applicable to whole data exploration platforms. These include components which are responsible for managing metadata (business and technical); also applies security control mechanisms such as data anonymization, role-based access etc, and overall platform data governance such as analyst onboarding, provisioning access, project portfolio management and so on Includes visual analytics tools which can connect, stream and visualize data hosted on both Big Data and traditional structured data repositories. Data virtualization layer, which is not shown in the diagram is leveraged to access data across different data repository tiers Embraces Big Data distribution components such as Interactive SQL, NoSQL data repositories, data indexing and search, and in-memory computation engines. Technical components provide technical enablement for the functionalities outlined in the Data Transformation layer 05
6 Data Exploration Process: Modules and Flow Figure 2 describes a typical data wrangling framework and illustrates how a combination of exploration and visual analytics tools can jointly help data analysts to derive valuable business insights. Figure 4 complements the framework and explains how a typical data exploration platform built using modern age tools and technologies works. Ingest Prepare Transform Publish Figure 4 Typical data wrangling platform workflow using modern age tools and technologies Each of these modules along with their associated components is listed as follows: Module Definition Components / Features Ingest Prepare Transform Ingest, manage and register data set in data exploration tool Preview, interact and define transformation workflow Execute transformation on entire data set at scale Data set entitlement engine (authentication and authorization) Exploration project / Workspace management Data ingestion: Connectors / APIs for source systems such as enterprise applications, S3, HDFS, NoSQL, etc. Data indexing, profiling and search Metadata catalog Data storage: NoSQL (Accumulo, Hbase, etc.), HDFS Data and metadata life cycle management: On premise, Cloud Data sampling: Sample data set Data security: Role-based data access Data lineage: Processing steps and metadata (application and operational) Data preparation and processing services UX framework components: Data preparation (structure, split, enrich, filter, etc.) Interactive access and data cache User access management Machine learning recommendations Data processing services: Entire data set Data lineage: Data processing steps and metadata (application and operational) Machine learning recommendations Off-Heap data cache considerations Batch data processing using MapReduce (Pig, Hive, etc.), Spark, and so on Publish Select transformation output format and target location Output data generation and persistence: HDFS, Avro, ORC, Parquet, etc. Connectors for downstream applications: BI, data services, mobile, enterprise applications Visual analytics tool connectors: Hadoop connectors, Star Schema, etc. Analytic SQL Impala, Hive, etc Scheduling and delivery: s, SFTP, etc 06
7 A success story Client brief: A large multinational company in the US Background: ValueLabs designed a data exploration platform to support the client s data analysts with a workflow-based, interactive data preparation, transformation and visualization platform. Data sets were ingested from a variety of sources (legacy systems that had been running for years) and across geographies involving high-volume data and complex, algorithm-driven data preparation recommendations. Some key features that were implemented include: Compute various KPIs (to be reported to customers) based on the ingested survey data Load data into target data model, providing a mechanism of sending Alerts to configured stakeholders (post the final loading of data) Create reports on top of the data store to report KPIs across store, product and time dimensions Challenges: Handling data ingestion from client s global data factory Dissimilar client data - Data in different formats / schema for different geographies and products Varied metadata across different products and geographies Solution: The main challenge was to handle dynamic schema and dynamic KPI calculation, and so we used the Avro Framework to handle dynamic schema, stored rules in HBase, and computed on Spark to achieve dynamic calculations. Key considerations: Configurable source file schema: Easy-to-handle changes in the existing source file schema or introduction of a new source file leveraging the flexible schema features of Avro Spark components: In-memory processing to speed up KPI calculation Avro Schema file: Helps in identifying the changes in source file, and in appropriate error handling Individual suite of applications or instances: For better control and customization Lineage / Job status: Lineage / job status information stored in a log file or HBase table to track the progress and status of components Key technologies considered: Configurable source file schema (using Avro) Spark components (for in-memory processing) Benefits: A platform that can be used to load as-is data from different data factories Linear scalability and performance improvement in data processing through dynamic schema handling and KPI calculation Quicker turnaround time for report generation while maintaining consistency in what gets reported Our solution included developing a metadata-driven data exploration system with the ability to compute KPIs with minimal changes in the code base, based on ever changing business needs. 07
8 Conclusion References In this paper, we have covered the practical problems and challenges that come up regularly when a data analyst tries to work with a real-world data set. Visualization can aid in the detection of potential problems in the raw data as a counterpart to fully algorithmic approaches. There is a possibility of integrated approaches that allow an individual to visually steer statistical algorithms. Visualization is also useful in the communication of data errors and uncertainties. When designing new visual interfaces, there needs to be a consideration for input data which may not be pristine. Also, the chosen visual interface should indicate any missing values and data uncertainties. Finally, when it comes to correcting data errors, visual approaches could integrate with automated approaches to allow an interactive editing cycle. Sean Kandel, Jeffrey Heer, Catherine Plaisant, Jessie Kennedy, Frank van Ham, Nathalie Henry Riche, Chris Weaver, Bongshin Lee5, Dominique Brodbeck and Paolo Buono, Research directions in data wrangling: Visualizations and transformations for usable and credible data, 2011 Trifacta, The Opportunity for Data Wrangling in Financial Services and Insurance Trifacta, The Opportunity for Data Wrangling in Life Sciences and Biopharmaceuticals Trifacta, Optimize Retail & CPG processes with Agile Data Wrangling on Hadoop Ideally, the output of an exploration session should be more than a clean data set; it should also encompass the raw data coupled with a well-defined set of data operations and potentially some metadata indicating why these operations were performed. These operations should be auditable and editable by a user. Secondary benefits of a high-level data transformation language include easier reuse of previous formatting efforts and an increased potential for social, distributed collaboration around data exploration. By Shuvadeep Dutta (Director - Architecture and Consulting) Manidipa Mitra (Director - Big Data) Debasish Chatterjee (VP and Global Head - Big Data) Plot No.41, Hitech City, Phase II, Cyberabad, Phone , Fax
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