How to Make Data-Driven Decisions for Effective Business Operations

 Modern Data Lake

Data Lake solutions first began to appear as a result of technological advancements like Big Data, but to a greater extent Cloud has driven their development. Data engineering solutions popularity can be attributed to its capacity for faster data retrieval than Data Warehouses, the elimination of a sizable amount of modeling work, the release of advanced analytics capabilities for an organization, and the addition of storage and compute scalability to handle a variety of workloads.

One of the main objectives of today's data platforms is the democratization of data. Any data platform's top priority is to provide self-service access to trustworthy data to end users including data analysts, data scientists, and business users. In order to help data lakes serve a wider audience and to boost consumer adoption, this blog analyzes the major trends we observe with our clients and in the industry.

Managing a Modern Data Lake Self-Service

The first step in fully utilizing data is to construct reliable, scalable data pipelines. However, the use of automation and self-serving features is what actually aids in accomplishing that goal. It will also aid in democratizing access to data, platforms, and analytics for all types of users and greatly lighten the load on IT teams, allowing them to concentrate on tasks of higher value.

These reusable parts and frameworks are utilized in the construction of data pipelines. These pipelines perform the data's import, management, transformation, and egress. Stopping here would deprive a company of the chance to use the data to its fullest advantage.

To maximize the outcome, APIs for data and platform management (developed in accordance with the microservices architecture) are made in order to carry out CRUD activities and for monitoring purposes. They can also be used to plan and start pipelines, find and manage datasets, manage clusters, maintain security, and manage users. Once the APIs have been established, you can create a web-based user interface that can coordinate all of these tasks and allow any user to browse it, bring in data, alter it, send it out, or manage it.

Data Cataloging Method

The growing usage of next-generation data engineering services is another typical trend we observe in contemporary data lakes. When working with several data sets and large volumes of data, a data catalog solution is useful. It has the ability to collect and decipher technical metadata from numerous datasets, link them together, examine their dependability, health, and usage patterns, and facilitate insight production for any user, including data scientists, analysts, and business analysts.

Although data catalogs have been around for a while, they are increasingly becoming more information-smart. More than merely adding technical metadata is now required.

Activation of the Data Catalog

Utilizing knowledge graphs and potent search tools is one of the key components of creating a data catalog. The information of a dataset, such as its schema, data quality, profiling statistics, PII, and classification, can be added to a knowledge graph solution. Additionally, it may identify the owner of a specific dataset, the users who are accessing it from different logs, and the department to which the user belongs.

Search and filter operations, graph queries, recommendation functions, and visual explorations can all be done using this knowledge graph.

Platform Observability for Data

We examines Observability in three phases:

1. Standard Health Monitoring Data

2. Prediction-based Advanced Data Health Monitoring

3. Adding Platform Observability 

Basic Health Monitoring Data

The most fundamental component of data health monitoring is identifying critical data elements (CDE) and keeping track of them. We set up rule-based data checks against these CDEs, record the outcomes on a regular basis, and offer visibility via dashboards. As much as possible, these problems are then addressed in source after being tracked through ticketing systems. The first step in establishing Data Observability is this method.

Monitoring the health of advanced data with predictions

The majority of the business clients with which we deal have passed the Basic Data Health Monitoring stage and are seeking to move on. It is necessary to improve the observability ecosystem with a few crucial features that will aid in the transition from a reactive to a more proactive reaction. The most recent technologies used for this purpose are artificial intelligence and machine learning. The ability to measure data drift and schema drift, classify incoming information automatically using AI/ML, identify and process personally identifiable information (PII) automatically, assign security entitlements automatically based on similar elements in the data platform, and more are just a few of the key capabilities. These features will improve the data's quality and provide early warning of data patterns.

Platform Observability Extension

Delivering accurate data to customers on schedule is data engineering solutions ultimate goal. This objective can only be met when we look beyond data observability and into the platform that really provides the data. This platform must be cutting-edge and up to date in order to transmit the data promptly and allow engineers and administrators to analyze and troubleshoot any issues. The primary features that we need to consider in order to increase platform-level observability are listed below.

Monitoring Data Flows & Environment: Capability to keep track on historical resource utilization patterns, server health, and job performance degradation in real-time.

Monitor Performance: In complicated data processing systems, it would be highly beneficial to understand how data flows from one system to another and look for bottlenecks in a visual way.

Keep an eye on data security: To make sure that data isn't being misused, it's important to keep an eye on query logs, access patterns, security tools, and other factors.

Analyze Workloads: Automatically identifying the problems and limitations that slow down massive data workloads and developing tools for Root Cause Analysis.

Foresee Problems, Find Answers: By comparing past performance to current operational effectiveness in terms of speed and resource utilization, it is possible to foresee problems and provide solutions.

Conclusion

The value of data democratization is what drives the Modern Data Lake environment. Accessibility to data management and insight gathering for all types of end users is crucial. The most promising approach for successful data engineering services are supported by intelligent data catalogs. Additionally, it is crucial to help data consumers develop their trust in the data. The discussed capabilities, such Data and Platform Observability, provide users genuine, behind-the-scenes control over the collection, processing, and delivery of data to various consumers. Today, businesses are working to develop end-to-end observability solutions since they are the ones that will advance the adoption and democratization of data platforms.