DataKitchen

AUGUST 3, 2021

First-generation – expensive, proprietary enterprise data warehouse and business intelligence platforms maintained by a specialized team drowning in technical debt. Second-generation – gigantic, complex data lake maintained by a specialized team drowning in technical debt. See the pattern? The problem is not “you.”

Data Architecture 246

Data Architecture Data Lake Cost-Benefit Data Warehouse 246

AWS Big Data

FEBRUARY 29, 2024

To provide a response that includes the enterprise context, each user prompt needs to be augmented with a combination of insights from structured data from the data warehouse and unstructured data from the enterprise data lake. Implement data privacy policies. Implement data quality by data type and source.

Data Governance 99

Data Governance Unstructured Data Metadata Data Lake 99

Jedox

OCTOBER 22, 2020

The term “data analytics” refers to the process of examining datasets to draw conclusions about the information they contain. Data analysis techniques enhance the ability to take raw data and uncover patterns to extract valuable insights from it. Data analytics is not new.

Data Analytics 69

Data Analytics Analytics Unstructured Data Data mining 69

AWS Big Data

APRIL 4, 2024

Griffin is an open source data quality solution for big data, which supports both batch and streaming mode. In today’s data-driven landscape, where organizations deal with petabytes of data, the need for automated data validation frameworks has become increasingly critical.

Data Quality 65

Data Quality Data Lake Data Warehouse Data-driven 65

IBM Big Data Hub

DECEMBER 16, 2022

An enterprise data catalog does all that a library inventory system does – namely streamlining data discovery and access across data sources – and a lot more. For example, data catalogs have evolved to deliver governance capabilities like managing data quality and data privacy and compliance.

Metadata 52

Metadata Data Quality Data-driven Data Governance 52

AWS Big Data

JUNE 6, 2023

In this post we showcase how we used AWS Glue to move siloed digital analytics data, with inconsistent arrival times, to AWS S3 (our Data Lake) and our central data warehouse (DWH), Snowflake. AWS Glue gave us a cost-efficient option to migrate the data and we further optimized storage cost by pruning cold data.

Analytics 56

Analytics Data Lake Testing Optimization 56

AWS Big Data

JANUARY 30, 2023

Many customers need an ACID transaction (atomic, consistent, isolated, durable) data lake that can log change data capture (CDC) from operational data sources. There is also demand for merging real-time data into batch data. Delta Lake framework provides these two capabilities.

Insurance 81

Insurance Data Lake Data-driven Management 81

IBM Big Data Hub

JUNE 8, 2022

However, a foundational step in evolving into a data-driven organization requires trusted, readily available, and easily accessible data for users within the organization; thus, an effective data governance program is key. Here are a few common data management challenges: Regulatory compliance on data use.

Data-driven 57

Data-driven Enterprise Data Governance Data Lake 57

AWS Big Data

NOVEMBER 9, 2023

Building a data platform involves various approaches, each with its unique blend of complexities and solutions. In this post, we delve into a case study for a retail use case, exploring how the Data Build Tool (dbt) was used effectively within an AWS environment to build a high-performing, efficient, and modern data platform.

Data Warehouse 109

Data Warehouse Testing Data Quality Reporting 109

DataKitchen

DECEMBER 9, 2022

ChatGPT> DataOps, or data operations, is a set of practices and technologies that organizations use to improve the speed, quality, and reliability of their data analytics processes. Overall, DataOps is an essential component of modern data-driven organizations.

Machine Learning 130

Machine Learning Data-driven Optimization Modeling 130

FineReport

MARCH 24, 2024

Rapid technological advancements and extensive networking have propelled the evolution of data analytics, fundamentally reshaping decision-making practices across various sectors. In this landscape, data analysts assume a pivotal role, tasked with interpreting data to drive informed decision-making.

Statistics 52

Statistics Data mining Visualization Reporting 52

AWS Big Data

JULY 20, 2023

With data becoming the driving force behind many industries today, having a modern data architecture is pivotal for organizations to be successful. In this post, we describe Orca’s journey building a transactional data lake using Amazon Simple Storage Service (Amazon S3), Apache Iceberg, and AWS Analytics.

Data Lake 89

Data Lake Analytics Snapshot Optimization 89

BizAcuity

APRIL 14, 2022

Big Data technology in today’s world. Did you know that the big data and business analytics market is valued at $198.08 Or that the US economy loses up to $3 trillion per year due to poor data quality? quintillion bytes of data which means an average person generates over 1.5 Big Data Ecosystem.

Big Data 52

Big Data Data Analytics Management Unstructured Data 52

datapine

AUGUST 29, 2022

The saying “knowledge is power” has never been more relevant, thanks to the widespread commercial use of big data and data analytics. The rate at which data is generated has increased exponentially in recent years. Essential Big Data And Data Analytics Insights. million searches per day and 1.2

Big Data 263

Big Data Data Analytics Analytics Data mining 263

CIO Business Intelligence

MARCH 9, 2022

Applying artificial intelligence (AI) to data analytics for deeper, better insights and automation is a growing enterprise IT priority. But the data repository options that have been around for a while tend to fall short in their ability to serve as the foundation for big data analytics powered by AI.

Data Lake 115

Data Lake Unstructured Data Data Warehouse Data Quality 115

Alation

MARCH 21, 2023

Why is data analytics important for travel organizations? With data analytics , travel organizations can gain real-time insights about customers to make strategic decisions and improve their travel experience. How is data analytics used in the travel industry?

Data Analytics 52

Data Analytics Analytics Data-driven Big Data 52

IBM Big Data Hub

JANUARY 17, 2023

A data lakehouse is an emerging data management architecture that improves efficiency and converges data warehouse and data lake capabilities driven by a need to improve efficiency and obtain critical insights faster. Let’s start with why data lakehouses are becoming increasingly important.

Data Lake 52

Data Lake Metadata Data Warehouse Data Governance 52

AWS Big Data

JULY 13, 2023

To succeed further, their vision of data requires it to be accessible and actionable to all, with key roles and responsibilities defined by those who produce and consume data, as shown in the following figure. Data quality for account and customer data – Altron wanted to enable data quality and data governance best practices.

Optimization 92

Optimization B2B Data Quality Sales 92

AWS Big Data

MARCH 1, 2023

Finding similar columns in a data lake has important applications in data cleaning and annotation, schema matching, data discovery, and analytics across multiple data sources. Parquet is a column-oriented format file format preferred in big data analytics that provides efficient compression and encoding.

Data Lake 88

Data Lake Deep Learning Modeling Interactive 88

AWS Big Data

MARCH 26, 2024

A Gartner Marketing survey found only 14% of organizations have successfully implemented a C360 solution, due to lack of consensus on what a 360-degree view means, challenges with data quality, and lack of cross-functional governance structure for customer data. You need to process this to make it ready for analysis.

Data Strategy 71

Data Strategy Strategy Data Warehouse Prescriptive Analytics 71

datapine

APRIL 15, 2020

When encouraging these BI best practices what we are really doing is advocating for agile business intelligence and analytics. These basic steps will enable you to deliver agile data analytics and BI methodology into practice, no matter the size of your company. Pilot release to small subgroup. Train end-users.

Business Intelligence 276

Business Intelligence Analytics Testing Dashboards 276

Alation

JULY 19, 2022

As they attempt to put machine learning models into production, data science teams encounter many of the same hurdles that plagued data analytics teams in years past: Finding trusted, valuable data is time-consuming. Obstacles, such as user roles, permissions, and approval request prevent speedy data access.

Metadata 59

Metadata Data Quality Statistics Data Science 59

DataKitchen

OCTOBER 11, 2021

The final step is designing a data solution and its implementation. The biggest challenge is broken data pipelines due to highly manual processes. Figure 1 shows a manually executed data analytics pipeline. Figure 2: Example data pipeline with DataOps automation. Second, you must establish a definition of “done.”

Testing 152

Testing Metadata Dashboards Statistics 152

CIO Business Intelligence

NOVEMBER 28, 2023

Making the most of enterprise data is a top concern for IT leaders today. With organizations seeking to become more data-driven with business decisions, IT leaders must devise data strategies gear toward creating value from data no matter where — or in what form — it resides. Tie initiatives to business outcomes.

Unstructured Data 129

Unstructured Data Data-driven Visualization Data Quality 129

IBM Big Data Hub

APRIL 27, 2022

Read: The first capability of a data fabric is a semantic knowledge data catalog, but what are the other 5 core capabilities of a data fabric? A data fabric also streamlines deriving insights from data through better data observability and data quality by automating manual tasks across data platforms using machine learning.

Management 83

Management Metadata Data Architecture Data Lake 83

CIO Business Intelligence

MARCH 22, 2022

But the sheer volume of the world’s data is expected to nearly triple between 2020 and 2025 to a whopping 180 zettabytes. How do you introduce AI into your data and analytics infrastructure? To companies entrenched in decades-old business and IT processes, data fiefdoms, and legacy systems, the task may seem insurmountable.

Analytics 111

Analytics Key Performance Indicator Data Warehouse Data-driven 111

AWS Big Data

DECEMBER 18, 2023

Solution overview One of the common functionalities involved in data pipelines is extracting data from multiple data sources and exporting it to a data lake or synchronizing the data to another database. The other step in the parallel state to fetch the DynamoDB table ran successfully.

Metadata 123

Metadata Visualization Data Lake Data-driven 123

IBM Big Data Hub

DECEMBER 7, 2023

C-OLAP optimized data storage for faster query processing, while IM-OLAP stored data in memory to minimize data access latency and enable real-time analytics. Today, OLAP database systems have become comprehensive and integrated data analytics platforms, addressing the diverse needs of modern businesses.

OLAP 60

OLAP Slice and Dice Cost-Benefit Data Warehouse 60

Cloudera

AUGUST 4, 2021

With hackers now working overtime to expose business data or implant ransomware processes, data security is largely IT managers’ top priority. And if data security tops IT concerns, data governance should be their second priority. Effective data governance must extend beyond the IT organization.

Data Governance 83

Data Governance Metadata Data-driven Enterprise 83

AWS Big Data

JULY 12, 2023

Thousands of customers use Amazon Redshift to analyze exabytes of data and run complex analytical queries. Amazon Redshift Serverless makes it straightforward to run and scale analytics in seconds without having to manage the data warehouse.

Data Warehouse 52

Data Warehouse Modeling Dashboards Data Lake 52

AWS Big Data

MARCH 3, 2023

Teams may struggle to have a unified view of software quality due to siloed testing across many disparate tools. Although Tricentis has amassed such data over a decade, the data remains untapped for valuable insights. This is a guest post co-written with Parag Doshi, Guru Havanur, and Simon Guindon from Tricentis.

Software 52

Software Data Lake Testing Cost-Benefit 52

AWS Big Data

NOVEMBER 15, 2023

As the organization receives data from multiple external vendors, it often arrives in different formats, typically Excel or CSV files, with each vendor using their own unique data layout and structure. DataBrew is an excellent tool for data quality and preprocessing.

Metadata 87

Metadata Sales Data Lake Big Data 87

IBM Big Data Hub

MARCH 14, 2024

Businesses face significant hurdles when preparing data for artificial intelligence (AI) applications. The existence of data silos and duplication, alongside apprehensions regarding data quality, presents a multifaceted environment for organizations to manage.

Cost-Benefit 79

Cost-Benefit Metadata Optimization Management 79

AWS Big Data

JUNE 12, 2023

Organizations across the world are increasingly relying on streaming data, and there is a growing need for real-time data analytics, considering the growing velocity and volume of data being collected. Next, an AWS Glue streaming ETL (extract, transform, and load) job is set up to process the incoming data.

Management 81

Management Metadata Testing Internet of Things 81

IBM Big Data Hub

OCTOBER 12, 2023

Once trained and deployed, models also need reliable access to historical and real-time data to generate content, make recommendations, detect errors, send proactive alerts, etc. They’re built on machine learning algorithms that create outputs based on an organization’s data or other third-party big data sources.

Cost-Benefit 79

Cost-Benefit Metadata Data Governance Modeling 79

Ontotext

FEBRUARY 26, 2024

The goal: a data-driven organization Let’s zoom out for a bit. Every organization wants to be data-driven. The goal is to capture data, convert it into the right insights, and integrate those insights quickly and efficiently into your business decisions and processes. Becoming data-driven will also be slow and costly.

Technology 52

Technology Cost-Benefit Data-driven Metadata 52

Cloudera

JANUARY 22, 2019

Forrester describes Big Data Fabric as, “A unified, trusted, and comprehensive view of business data produced by orchestrating data sources automatically, intelligently, and securely, then preparing and processing them in big data platforms such as Hadoop and Apache Spark, data lakes, in-memory, and NoSQL.”.

Big Data 44

Big Data Data Lake Internet of Things Enterprise 44