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Banking & Finance

Data Lake Implementation for Efficient Big Data Processing and Visualization

Data Lake Implementation
Key Details
  • Challenge
    Find an efficient approach to data gathering, analyzing, processing, visualization
  • Solution
    BI solution to enable real-time monitoring of workflows across an organization
  • Technologies and tools
    Apache NiFi, Cloudera CDH, Apache Oozie, Apache Spark, HDFS (raw, parquet), Apache Kudu, Apache Impala

Client

The Client is a UK-based global construction services company that assists different industry representatives in managing their business data. The Client’s solution is an AI-driven platform allowing end users to employ the analytics engine to extract value from raw business data. This platform assists in building creative business relationships and uncovering new business opportunities.

Challenge: find an efficient approach to data gathering, analyzing, processing, visualization

Since operating in various domains, including finance, health, management, consulting, etc., the Client inevitably faced the challenge of managing large volumes of unstructured raw data and turned to InData Labs with the need to build data lakes and deliver a BI solution for more efficient data processing. The Client needed a solution to facilitate such processes as data gathering, analysis, and visualization.

Solution: Business intelligence solution to enable real-time monitoring of workflows across an organization

One of the Client’s domains is banking & finance. Business owners in this industry are continually on the lookout for a reliable and consistent approach to manage data. Such an approach underpins all the processes related to managing legal documents, corporate documents, client agreements, etc., to eliminate errors and build consumers trust.

Businesses cooperate with the Client to be able to leverage an AI-powered solution for answering key business questions. A data-driven solution requires the involvement of data scientists to bring in their experience and expertise in working with big data.

When stored in a data lake, data provided by end users becomes available to AI and can be exploited to provide end users with valuable insight on various queries related to risk management, teamwork, project status, working schedule, downtimes, accident management, and more.

A streamlined and optimized way of processing data is the kernel of an AI-powered solution. InData Labs was challenged to provide data science services to enhance the performance and efficiency of the Client’s solution.

The InData Labs’ team started step-by-step development process to solve the Client’s challenge and deliver a robust solution.

1. UK financial organizations have adopted a document supply chain, encompassing the following stages:

  • Document gathering
  • Accumulation of documents
  • Order processing

Documents come from multiple sources and in different formats. The chaotic nature of gathered documents and the lack of common repository prevente extracting value from big data for further important uses.

2. The InData Labs’ team worked with unstructured data from different sources, such as some listed below:

  • Project Data
  • Meetings
  • Digital Identity
  • Work Schedule
  • Inspection Data
  • Hazard Data
  • Etc.

It was needed to provide the solution to turn raw big data into meaningful insight.

3. InData Labs developed data lakes to aggregate raw data in different formats and store data in files. We used the following open-source services to make the data available for analysis and visualization:

  • Apache NiFi for data ingestion
  • Cloudera CDH as a data management platform
  • Apache Oozie for data processing workflow
  • Apache Spark as a data processing engine
  • HDFS (raw and parquet) for data storage
  • Apache Kudu for data analytics
  • Apache Impala for data analytics

The data lake implementation helped structure business data, which then allowed using open-source components to deliver a BI solution to meet the Client’s needs.

4. Since end users require smooth access to data in visually appealing forms, the Client’s solution provides a user-friendly interface. The processed and classified data becomes available via easy-to-interact dashboards. These dashboards utilize data from the data lakes to enable comprehensive insights. As a result, the solution facilitates real-time monitoring of business workflows across an organization and provides visual insight for better decision making.

Result: improved KPIs and impetus to data-driven business development

The Client provided the InData Labs team with raw data to be used for developing a BI solution. Based on the data, presented by the Client, and by using open-source components, InData Labs tailored and delivered an MVP to address core needs of end users while working with databases and the needs of consumers of the Client’s services in an efficient BI solution.

The data lake solution empowers both employees and managers working in the industry to make accelerated data-driven decisions, stay abreast of market trends, achieve better KPIs, pinpoint new sources of revenue and business opportunities.

Our team provided ready-to-use data services to help end users solve business-critical issues and foster continuous business processes improvement.

Tags:
  • Implementation
  • Big Data
  • Data Lake
  • BI

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