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The Importance of Big Data in Web Development Strategy

The current public health crisis continues to disrupt industries across the globe. One of the biggest lessons that are coming out of this ordeal is the crucial role that technology plays in building resilience. Big data, perhaps one of the biggest faces of technological disruption over the past few years, has played a central role in helping startups stay alive, even before the pandemic.

For web developers and web development startups, big data can be a game-changer. Big data enables developers to infuse critical insights from data analysis, which helps build data-driven applications that boost user experience.

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Big Data Testing: The Solution to Deal With Volume, Velocity, and Variety

Big Data typically refers to data that is more than one terabyte. Along with high volume, it is also characterized by high velocity and variety. As it includes different variety of formats, including structured, unstructured, and semi-structured, the testing of such Big Data has to be defined accordingly. With huge volumes of data getting generated in most processes, Big Data Solutions and Big Data Testing is becoming the trend ahead.

Stages in Big Data Testing

Big Data Testing primarily comprises three broad-level stages:

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Microsoft Azure Data Lake

2020 is different in every way, but one thing is constant for the past many years i.e. data and its role in molding our current technology. Recently, I was part of the team to create a central controlled data repository containing clear, consistent, and clean data. While exploring the technologies we landed on MS Azure echo system.

MS Azure echo system for developing data lakes/data warehouse is becoming mature and providing good support when it comes to the enterprise-level solutions. Starting from Azure Data Factory, it gave a good ELT/ETL processing with code-free services. This is very helpful to create pipelines for data ingestion, control flow, and moving data from source to destination. These pipelines have the capability to run 24/7 and ingest petabytes of data. Without the support of a data factory data movement between different enterprise systems requires a lot of effort and at times will be very expensive to develop and maintain. Additionally, there are more than 90 built-in connectors in Azure Data Factory which will help to connect with most of the sources like S3, Redshift, BigQuery, HDFS, Salesforce, and enterprise data warehouse to name a few.  

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The Basics Of Web Scraping With Proxies

The worldwide web is a treasure trove of data. The availability of big data, lightning-fast development of data analytics software, and increasingly inexpensive computing power have further heightened the importance of data-driven strategies for competitive differentiation.

According to Forrester's report, data-driven companies that harness insights across their organization and implement them to create competitive advantage, are growing at an average of more than 30 percent annually and are on track to earn $1.8 trillion by 2021.

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Migrating Apache Flume Flows to Apache NiFi: Kafka Source to Multiple Sinks

The world of streaming is constantly moving... yes I said it. Every few years some projects get favored by the community and by developers. Apache NiFi has stepped ahead and has been the go-to for quickly ingesting sources and storing those resources to sinks with routing, aggregation, basic ETL/ELT, and security. I am recommending a migration from legacy Flume to Apache NiFi. The time is now.

Below, I walk you through a common use case. It's easy to integrate Kafka as a source or sink with Apache NiFi or MiNiFi agents. We can also add HDFS or Kudu sinks as well. All of this with full security, SSO, governance, cloud and K8 support, schema support, full data lineage, and an easy to use UI. Don't get fluming mad, let's try another great Apache project.

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A Brief Overview of Pandas DataFrames

Significantly more adorable... slightly less helpful for data wrangling

This article is the continuation of my previous article. Here, we will be discussing another datatype, Dataframes.

Dataframes are going to be the main tool that developers use when working with pandas.

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These Seven Non-Tech Domains Call Big Data the Big Daddy

In “Big Data: A Revolution That Will Transform How We Live, Work, and Think,” Viktor Mayer-Schönberger and Kenneth Cukier argue that “big data analytics is a revolutionary tool, used mainly in business, science, research, media industries, and social life.” I cannot argue more in favor of their analysis. The way big data has jumped the high walls of standard technology-based industries to the usefulness of other non-tech domains is fascinating.

Here are the seven industries in which big data is now the big daddy!

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Taking a Modern Approach to BI

When legacy Business Intelligence (BI) solutions emerged, the goal was to simplify data access and analysis across an entire company. Sadly, the benefits of these solutions were never realized. Decades later, companies still aren’t seeing the adoption they expected—meanwhile, billions have been spent on BI. Something is clearly missing.

According to IDC, worldwide revenues for big data and BI solutions will reach $260 billion in 2022. Yet, even with all this projected growth, Tableau’s & PowerBI's success, and Looker’s $103 million-dollar funding round, 88% of IT decision makers will choose Excel as the primary tool to explore company data in 2019. So what gives?