The Antidote for Congested Data and Analytics Pipelines

Suitable solutions should provide data scientists with toolkits that include integrated AI libraries and that support all the end-to-end data pipeline functions.

Imagine that you’ve got a top-notch finance team, but the only tools they have are pen and paper. Thinking along these lines, imagine your sales team works from Rolodexes or marketing only has dial-up internet. Now think about how your data science team works.

For your data science team to accomplish great things, businesses need tools and systems that cover the entire range of operations when it comes to turning data into insights. What’s needed are tools that manage everything from data ingestion, preparation, visualization, and analysis. The tools need to support and, most importantly, facilitate the data science process. However, for many companies, the tools themselves are a huge barrier. Here’s what you need to know.

#tools and tactics #enterprise #machine learning

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The Antidote for Congested Data and Analytics Pipelines
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Your Data Architecture: Simple Best Practices for Your Data Strategy

If you accumulate data on which you base your decision-making as an organization, you should probably think about your data architecture and possible best practices.

If you accumulate data on which you base your decision-making as an organization, you most probably need to think about your data architecture and consider possible best practices. Gaining a competitive edge, remaining customer-centric to the greatest extent possible, and streamlining processes to get on-the-button outcomes can all be traced back to an organization’s capacity to build a future-ready data architecture.

In what follows, we offer a short overview of the overarching capabilities of data architecture. These include user-centricity, elasticity, robustness, and the capacity to ensure the seamless flow of data at all times. Added to these are automation enablement, plus security and data governance considerations. These points from our checklist for what we perceive to be an anticipatory analytics ecosystem.

#big data #data science #big data analytics #data analysis #data architecture #data transformation #data platform #data strategy #cloud data platform #data acquisition

Gerhard  Brink

Gerhard Brink


Getting Started With Data Lakes

Frameworks for Efficient Enterprise Analytics

The opportunities big data offers also come with very real challenges that many organizations are facing today. Often, it’s finding the most cost-effective, scalable way to store and process boundless volumes of data in multiple formats that come from a growing number of sources. Then organizations need the analytical capabilities and flexibility to turn this data into insights that can meet their specific business objectives.

This Refcard dives into how a data lake helps tackle these challenges at both ends — from its enhanced architecture that’s designed for efficient data ingestion, storage, and management to its advanced analytics functionality and performance flexibility. You’ll also explore key benefits and common use cases.


As technology continues to evolve with new data sources, such as IoT sensors and social media churning out large volumes of data, there has never been a better time to discuss the possibilities and challenges of managing such data for varying analytical insights. In this Refcard, we dig deep into how data lakes solve the problem of storing and processing enormous amounts of data. While doing so, we also explore the benefits of data lakes, their use cases, and how they differ from data warehouses (DWHs).

This is a preview of the Getting Started With Data Lakes Refcard. To read the entire Refcard, please download the PDF from the link above.

#big data #data analytics #data analysis #business analytics #data warehouse #data storage #data lake #data lake architecture #data lake governance #data lake management

Ian  Robinson

Ian Robinson


Top 10 Big Data Tools for Data Management and Analytics

Introduction to Big Data

What exactly is Big Data? Big Data is nothing but large and complex data sets, which can be both structured and unstructured. Its concept encompasses the infrastructures, technologies, and Big Data Tools created to manage this large amount of information.

To fulfill the need to achieve high-performance, Big Data Analytics tools play a vital role. Further, various Big Data tools and frameworks are responsible for retrieving meaningful information from a huge set of data.

List of Big Data Tools & Frameworks

The most important as well as popular Big Data Analytics Open Source Tools which are used in 2020 are as follows:

  1. Big Data Framework
  2. Data Storage Tools
  3. Data Visualization Tools
  4. Big Data Processing Tools
  5. Data Preprocessing Tools
  6. Data Wrangling Tools
  7. Big Data Testing Tools
  8. Data Governance Tools
  9. Security Management Tools
  10. Real-Time Data Streaming Tools

#big data engineering #top 10 big data tools for data management and analytics #big data tools for data management and analytics #tools for data management #analytics #top big data tools for data management and analytics

Big Data Analytics: Unrefined Data to Smarter Business Insights -

For Big Data Analytics, the challenges faced by businesses are unique and so will be the solution required to help access the full potential of Big Data.
Let’s take a look at the Top Big Data Analytics Challenges faced by Businesses and their Solutions.

#big data analytics challenges #big data analytics #data management #data analytics strategy #business solutions by big data #top big data analytics companies

How to Define Data Analytics Capabilities | Hacker Noon

Disclaimer: Many points made in this post have been derived from discussions with various parties, but do not represent any individuals or organisations.

Defining clear roles, responsibilities and ways of working is very important. Although my other post has already described the Engine and the Driver, it is interesting to understand what capabilities should remain centralised and what should be decentralised for an organisation to become more effective in their data analytics journey.

Let’s start by looking at the essential functions required to facilitate a data-driven organisation.

  • Infrastructure - with a few exceptions of highly-regulated sectors, the direction for a data infrastructure has been moving towards the cloud. Thanks to the serverless architecture and container technology, the shift not only reduces the operational complexity but also allows for higher reliability, availability and scalability, which are essential attributes for a data platform.
  • Data Pipelines - a robust movement and processing of data from one point to another is required to make it suitable for consumption. A data pipeline can be either a simple ELT/ETL process or a complex orchestration including real-time streaming and modelling. The emergence of streaming engines such as StormFlink and Spark also makes real-time analysis easier.
  • Reporting and Analysis - the ultimate goals of getting into the data space is to gain additional values. It can be a simple process that turns data into informational summaries or a complex analysis that extracts meaningful insights in a descriptive, a predictive or a prescriptive way. The product of such reporting or analysis can be presented in different ways subject to the usability and functionality requirements.
  • Other Functions - security and governance are considered intrinsic functions to the data platform. Access controls and appropriate policies must be in place to safeguard against attacks and unintended usage of sensitive data. Suitable capabilities on monitoring, auditing and billing are also essential depending on the operational requirements of each organisation.

Before considering what capabilities should be decentralised or remain centralised, it is worth to understand what can happen under a different context.

#data #data-analytics #data-strategy #data-asset #agile-teams #business #data-pipeline #analytics