Analytics Vidhya

AUGUST 30, 2023

Introduction In the bustling arena of database management systems, two heavyweight contenders emerge, each carrying its arsenal of features and capabilities. In one corner, we have the suave and sophisticated Microsoft SQL Server (MSSQL), donned in the elegance of enterprise-level prowess. And in the other corner the scrappy and open-source MySQL, armed with its community-driven […] The post MSSQL vs MySQL: Comparing Powerhouses of Databases appeared first on Analytics Vidhya.

MySQL 228

MySQL Database SQL Systems 228

KDnuggets

AUGUST 30, 2023

7 Projects Built with Generative AI • Beyond Numpy and Pandas: Unlocking the Potential of Lesser-Known Python Libraries • 5 Ways You Can Use ChatGPT’s Code Interpreter For Data Science • GPT-4: 8 Models in One; The Secret is Out

Python 131

Python Project Data Science Coding 131

databricks

AUGUST 30, 2023

It’s no secret that modern organizations are doubling down on their investments in data - investments that uncover deep customer insights that provide a.

Data 122

Data 122

KDnuggets

AUGUST 30, 2023

This post discusses the importance of media mix modeling and how it can be used to maximize the business impact of advertising. It also discusses the impact of seasonality on media advertising and how media mix modeling can be used to minimize the impact of seasonality on business outcomes.

Media 109

Media IT Data Science Data 109

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Generative AI is upending the way product developers & end-users alike are interacting with data. Despite the potential of AI, many are left with questions about the future of product development: How will AI impact my business and contribute to its success? What can product managers and developers expect in the future with the widespread adoption of AI?

Data

Snowflake

AUGUST 30, 2023

In the past few days, the scope and trajectory of Instacart’s use of Snowflake has been misrepresented by some on social media. Snowflake has partnered closely with Instacart to scale up to meet the company’s massive demand growth, and then to optimize for efficiency. Optimizations are undertaken on a workload-by-workload basis, and have been extremely successful.

Media 115

Media Retail Machine Learning Data Pipeline 115

KDnuggets

AUGUST 30, 2023

Struggling to develop your coding routine? Well, I have some psychological insights to share that can boost your motivation and make a real difference in your coding journey.

Coding 102

Coding Programming 102

databricks

AUGUST 30, 2023

With the rapid advancement of neural network-based techniques and Large Language Model (LLM) research, businesses are increasingly interested in AI applications for value.

Engineering 89

Engineering 89

Confluent

AUGUST 30, 2023

Confluent Platform 7.5 brings SSO for Control Center, simplified interface with Confluent using v3 of the REST proxy API, and bidirectional Cluster Linking.

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78

databricks

AUGUST 30, 2023

At Databricks, we are constantly evolving to meet the ever-changing needs of our customers. This year, we launched a new role that aims.

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87

Confluent

AUGUST 30, 2023

Get a sneak peak into what awaits you at Current 2023—featuring captivating keynotes, must-attend sessions, networking opportunities, and much more.

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70

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Many organizations today are unlocking the power of their data by using graph databases to feed downstream analytics, enahance visualizations, and more. Yet, when different graph nodes represent the same entity, graphs get messy. Watch this essential video with Senzing CEO Jeff Jonas on how adding entity resolution to a graph database condenses network graphs to improve analytics and save your analysts time.

Database

Databand.ai

AUGUST 30, 2023

Data testing tools: Key capabilities you should know Helen Soloveichik August 30, 2023 Data testing tools are software applications designed to assist data engineers and other professionals in validating, analyzing and maintaining data quality. These tools play a vital role in data preparation, which involves cleaning, transforming and enriching raw data before it can be used for analysis or machine learning models.

Data Cleanse 52

Data Cleanse Data Pipeline Datasets Data Validation 52

Monte Carlo

AUGUST 30, 2023

Data testing is the first step in many data engineers’ journey toward reliable data. And one of the most popular approaches is dbt testing. dbt (data build tool) is a SQL-based command-line tool that offers native testing features. But there’s a lot to understand in order to both create the most value from your dbt tests and avoid leaning too heavily on a time-intensive process.

SQL 52

SQL Datasets Database High Quality Data 52

Databand.ai

AUGUST 30, 2023

Data Testing Tools: Key Capabilities and 6 Tools You Should Know Helen Soloveichik August 30, 2023 What Are Data Testing Tools? Data testing tools are software applications designed to assist data engineers and other professionals in validating, analyzing, and maintaining data quality. These tools play a vital role in data preparation, which involves cleaning, transforming, and enriching raw data before it can be used for analysis or machine learning models.

Data Cleanse 52

Data Cleanse Data Validation Data Pipeline Datasets 52

Snowflake

AUGUST 30, 2023

In today’s digital age, the growth and success of an enterprise heavily rely on how it manages and leverages its data. There are multiple enterprise data platforms in the market, each offering its distinct capabilities. However, when it comes to enterprise-grade requirements certain key features are indispensable. In this blog post, we will delve into six such capabilities – comprehensive cross-cloud replication, zero copy database and schema clone, collation support, stored procedures, mu

Scala 91

Scala Government Database Cloud 91

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Start-ups & SMBs launching products quickly must bundle dashboards, reports, & self-service analytics into apps. Customers expect rapid value from your product (time-to-value), data security, and access to advanced capabilities. Traditional Business Intelligence (BI) tools can provide valuable data analysis capabilities, but they have a barrier to entry that can stop small and midsize businesses from capitalizing on them.

Data Analysis

Databand.ai

AUGUST 30, 2023

Data Consistency vs Data Integrity: Similarities and Differences Joseph Arnold August 30, 2023 What Is Data Consistency? Data consistency refers to the state of data in which all copies or instances are the same across all systems and databases. Consistency ensures that data is accurate, up-to-date, and coherent across different database systems, applications, and platforms.

Data Integration 52

Data Integration Data Cleanse Data Validation High Quality Data 52

LinkedIn Engineering

AUGUST 30, 2023

One measure of a successful network is uptime - providing consistent, reliable service for members and customers. If there are frequent connection errors or downtime notifications, it becomes difficult to deliver an experience where people can connect and interact with ease. When faced with uptime challenges, being able to quickly escalate issues to network engineers helps ensure that people can work the way that they want to.

Database 90

Database Designing Architecture Systems 90

Databand.ai

AUGUST 30, 2023

DataOps Architecture: 5 Key Components and How to Get Started Ryan Yackel August 30, 2023 What Is DataOps Architecture? DataOps is a collaborative approach to data management that combines the agility of DevOps with the power of data analytics. It aims to streamline data ingestion, processing, and analytics by automating and integrating various data workflows.

Architecture 52

Architecture Data Ingestion Data Cleanse Data Governance 52

Databand.ai

AUGUST 30, 2023

Data Accuracy vs Data Integrity: Similarities and Differences Eric Jones August 30, 2023 What Is Data Accuracy? Data accuracy refers to the degree to which data is correct, precise, and free from errors. In other words, it measures the closeness of a piece of data to its true value. Data accuracy is a crucial aspect of data quality, as inaccurate data can lead to incorrect decision-making, poor customer service, and operational inefficiencies.

Data Integration 52

Data Integration Data Cleanse Data Validation Data Governance 52

Speaker: Scott Sehlhorst

We know we want to create products which our customers find to be valuable. Whether we label it as customer-centric or product-led depends on how long we've been doing product management. There are three challenges we face when doing this. The obvious challenge is figuring out what our users need; the non-obvious challenges are in creating a shared understanding of those needs and in sensing if what we're doing is meeting those needs.

Certification

Databand.ai

AUGUST 30, 2023

DataOps Framework: 4 Key Components and How to Implement Them Eric Jones August 30, 2023 What Is a DataOps Framework? The DataOps framework is a set of practices, processes, and technologies that enables organizations to improve the speed, accuracy, and reliability of their data management and analytics operations. DataOps is often likened to the DevOps and agile methodologies used in software development, as it aims to break down silos, encourage collaboration, and streamline workflows for data

Data Governance 52

Data Governance Data Pipeline Government Data Cleanse 52

Databand.ai

AUGUST 30, 2023

Building a Winning Data Quality Strategy: Step by Step Eitan Chazbani August 30, 2023 What Is a Data Quality Strategy? A data quality strategy details the processes, tools, and techniques employed to ensure your company’s data is accurate, consistent, complete, and up-to-date. A well-defined data quality strategy enables better decision-making based on reliable information and reduces risks associated with poor-quality data.

Building 52

Building Data Cleanse Data Governance Datasets 52

Databand.ai

AUGUST 30, 2023

7 Data Testing Methods, Why You Need Them & When to Use Them Helen Soloveichik August 30, 2023 What Is Data Testing? Data testing involves the verification and validation of datasets to confirm they adhere to specific requirements. The objective is to avoid any negative consequences on business operations or decisions arising from errors, inconsistencies, or inaccuracies.

Data Validation 52

Data Validation Data Integration Data Database 52

Databand.ai

AUGUST 30, 2023

DataOps Tools: Key Capabilities & 5 Tools You Must Know About Ryan Yackel August 30, 2023 What Are DataOps Tools? DataOps , short for data operations, is an emerging discipline that focuses on improving the collaboration, integration, and automation of data processes across an organization. DataOps tools are software solutions designed to simplify and streamline the various aspects of data management and analytics, such as data ingestion, data transformation, data quality management, data c

Data Cleanse 52

Data Cleanse Data Pipeline Data Ingestion Data Validation 52

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While data platforms, artificial intelligence (AI), machine learning (ML), and programming platforms have evolved to leverage big data and streaming data, the front-end user experience has not kept up. Holding onto old BI technology while everything else moves forward is holding back organizations. Traditional Business Intelligence (BI) aren’t built for modern data platforms and don’t work on modern architectures.

BI

Databand.ai

AUGUST 30, 2023

8 Data Quality Monitoring Techniques & Metrics to Watch Niv Sluzki August 30, 2023 What Is Data Quality Monitoring? Data quality monitoring refers to the assessment, measurement, and management of an organization’s data in terms of accuracy, consistency, and reliability. It utilizes various techniques to identify and resolve data quality issues, ensuring that high-quality data is used for business processes and decision-making.

Data Cleanse 52

Data Cleanse Metadata High Quality Data Datasets 52

Databand.ai

AUGUST 30, 2023

Unified DataOps: Components, Challenges, and How to Get Started Joseph Arnold August 30, 2023 What Is Unified DataOps? Unified DataOps represents a fresh approach to managing and synchronizing data operations across several domains, including data engineering, data science, DevOps, and analytics. The goal of this strategy is to streamline the entire process of extracting insights from raw data by removing silos between teams and technologies.

Data Governance 40

Data Governance Data Cleanse Government Data Pipeline 40