Data Preparation: The Case for Using Automated, ML-Based Tools

Data preparation has always been challenging, but over the past few years as companies increasingly indulge in big data technologies, data preparation has become a mammoth challenge threatening the success of big data, AI, IoT initiatives.

Unlimited data, but limited capacities have led enterprises to use data lakes – a new technology that stores all your data in its natural format.

Unlike data warehouses where the data is cleansed, prepared then stored, data lakes store data in its original form; unprocessed, unprepared, untouched.

In this piece, we’ll specifically talk about data preparation as the most critical challenge and how an ML-based data preparation tool or software can make it easier to process data in the data lake.

#big-data #data-preprocessing #data-quality #data-preparation #machine-learning #data-preparation-tools #latest-tech-stories #artificial-intelligence

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Data Preparation: The Case for Using Automated, ML-Based Tools

Data preparation has always been challenging, but over the past few years as companies increasingly indulge in big data technologies, data preparation has become a mammoth challenge threatening the success of big data, AI, IoT initiatives.

Unlimited data, but limited capacities have led enterprises to use data lakes – a new technology that stores all your data in its natural format.

Unlike data warehouses where the data is cleansed, prepared then stored, data lakes store data in its original form; unprocessed, unprepared, untouched.

In this piece, we’ll specifically talk about data preparation as the most critical challenge and how an ML-based data preparation tool or software can make it easier to process data in the data lake.

#big-data #data-preprocessing #data-quality #data-preparation #machine-learning #data-preparation-tools #latest-tech-stories #artificial-intelligence

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

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 Architecture: Layers, Patterns, Use Cases and Tools

Introduction to Big Data Architecture

Big Data Architecture helps design the Data Pipeline with the various requirements of either the Batch Processing System or Stream Processing System. This architecture consists of 6 layers, which ensure a secure flow of data.

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Big Data Architecture Layers

• Data Ingestion Layer
• Data Collector Layer
• Big Data Processing Layer
• Data Storage Layer
• Data Query Layer
• Big Data Visualization Layer
• Data Security Layer
• Data Monitoring Layer

#big data engineering #blogs #big data architecture: layers, patterns, use cases and tools #big data architecture #use cases and tools #patterns

Top 10 Big Data Tools for 2021!

In today’s tech world, data is everything. As the focus on data grows, it keeps multiplying by leaps and bounds each day. If earlier mounds of data were talked about in kilobytes and megabytes, today terabytes have become the base unit for organizational data. This coming in of big data has transformed paradigms of data storage, processing, and analytics.

Instead of only gathering and storing information that can offer crucial insights to meet short-term goals, an increasing number of enterprises are storing much larger amounts of data gathered from multiple resources across business processes. However, all this data is meaningless on its own. It can add value only when it is processed and analyzed the right way to draw point insights that can improve decision-making.

Processing and analyzing big data is not an easy task. If not handled correctly, big data can turn into an obstacle rather than an effective solution for businesses. Effective handling of big data management  requires to use of tools that can steer you toward tangible, substantial results. For that, you need a set of great big data tools that will not only solve this problem but also help you in producing substantial results.

Data storage tools, warehouses, and data lakes all play a crucial role in helping companies store and sort vast amounts of information. However, the true power of big data lies in its analytics. There are a host of big data tools in the market today to aid a business’ journey from gathering data to storing, processing, analyzing, and reporting it. Let’s take a closer look at some of the top big data tools that can help you inch closer to your goal of establishing data-driven decision-making and workflow processes.

Apache Hadoop

Apache Spark

Flink

Apache Storm

Apache Cassandra

…

#big data #big data tools #big data management #big data tool #top 10 big data tools for 2021! #top-big-data-tool