Apps Associates

MAY 18, 2021

Performing analytics on the data was possible but took a long time and was mostly done in batch (using map reduce routines written in Java). Although on-premises Hadoop clusters ushered in the era of Big Data and supported data use cases that previously were not possible, many of these systems proved to be costly and cumbersome to maintain.

Cloud 98

Cloud Data Azure Analytics 98

Perficient

FEBRUARY 28, 2023

In blog 2 of our data lakehouse blog series , we discussed how Perficient’s Healthy Lakehouse solution accelerates time and cost to value, and can help crack the code to success in delivering solutions. Next, we’d like to share some real-life examples of projects and solutions we’ve executed with our clients.

Healthcare 64

Healthcare Technical Review AWS Analytics 64

Cloudera

JUNE 18, 2022

Cloudera customers run some of the biggest data lakes on earth. These lakes power mission critical large scale data analytics, business intelligence (BI), and machine learning use cases, including enterprise data warehouses. Today, the Hive metastore is used from multiple engines and with multiple storage options.

Data 92

Data Analytics Open Source Architecture 92

Datavail

JULY 7, 2022

Meanwhile, in an informal survey of attendees at a recent Datavail webinar, the majority (75 percent) of attendees said that their organization was pursuing a “hybrid” (partly on-premises and partly in the cloud) strategy for business intelligence and analytics. High data volumes.

Analytics 52

Analytics Artificial Inteligence Cloud Artificial Intelligence 52

Cloudera

MAY 15, 2019

He is a successful architect of healthcare data warehouses, clinical and business intelligence tools, big data ecosystems, and a health information exchange. The Enterprise Data Cloud – A Healthcare Perspective. Check out this list below to see some of them in action: Comcast.

Enterprise 48

Enterprise Cloud Data Healthcare 48

Cloudera

AUGUST 31, 2021

While this “data tsunami” may pose a new set of challenges, it also opens up opportunities for a wide variety of high value business intelligence (BI) and other analytics use cases that most companies are eager to deploy. . Separated Big Data cluster from other programs for Data Science / Discovery to isolate workloads.

Data 82

Data Analytics Cloud Technical Review 82

Infinidat

MARCH 10, 2021

Re-Thinking the Storage Infrastructure for Business Intelligence. With digital transformation under way at most enterprises, IT management is pondering how to optimize storage infrastructure to best support the new big data analytics focus. Adriana Andronescu. Wed, 03/10/2021 - 12:42.

Business Intelligence 98

Business Intelligence Storage Infrastructure Artificial Inteligence 98

Altexsoft

AUGUST 19, 2022

In our blog, we’ve been talking a lot about the importance of business intelligence (BI), data analytics, and data-driven culture for any company. Users can easily create a wide range of data-intensive, yet intelligible reports and dashboards and share obtained insights. What is Power used for?

Weak Development Team 52

Weak Development Team Data Azure Analytics 52

Datavail

MARCH 24, 2020

Loosely defined, a data strategy is a list of high-level objectives and priorities for your enterprise data assets. This includes your plans for handling data at every stage of the pipeline, from collection and storage to integration, analysis, and collaboration.

Strategy 40

Strategy Data Policies Storage 40

Altexsoft

MAY 14, 2021

Big Data enjoys the hype around it and for a reason. But the understanding of the essence of Big Data and ways to analyze it is still blurred. This post will draw a full picture of what Big Data analytics is and how it works. Big Data and its main characteristics. Key Big Data characteristics.

Big Data 78

Big Data Analytics Tools Applications 78

Kaseya

NOVEMBER 9, 2023

Its proliferation across businesses is a testament to its undeniable advantages, offering a dynamic ecosystem wherein organizations can seamlessly scale and streamline operations, foster innovation and adapt swiftly to ever-evolving market demands.

Cloud 105

Cloud Disaster Recovery Artificial Inteligence Infrastructure 105

Perficient

JULY 4, 2023

The process involves extracting data from the source systems, transforming it into a format that can be used by the destination system, and then loading it into the destination system. This approach is often used when the destination system has the capability to perform complex transformations and data manipulation. What is ELT?

Big Data 52

Big Data Scalability Analysis Performance 52

Perficient

DECEMBER 19, 2022

In this Blog, we look at Power BI vs. Tableau, exploring their similarities and differences and their pros and cons. According to data from Gartner , Power BI and Tableau are the first- and second-most popular business intelligence tools on the market. Power BI and Tableau are both built to handle large amounts of data.

UI/UX 64

UI/UX Analytics Software Review Business Intelligence 64

Mobilunity

NOVEMBER 24, 2021

In most digital spheres, especially in fintech, where all business processes are tied to data processing, a good big data engineer is worth their weight in gold. In this article, we’ll discuss the role of an ETL engineer in data processing and why businesses need such experts nowadays. Manage ETL processes.

Development 52

Development Storage Recruiting Architecture 52

Altexsoft

FEBRUARY 10, 2021

New approaches arise to speed up the transformation of raw data into useful insights. Similar to how DevOps once reshaped the software development landscape, another evolving methodology, DataOps, is currently changing Big Data analytics — and for the better. The core difference lies in the point where raw data is transformed.

Analytics 75

Analytics DevOps Development Software Review 75

Cloudera

NOVEMBER 2, 2020

Correlations across data domains, even if they are not traditionally stored together (e.g. real-time customer event data alongside CRM data; network sensor data alongside marketing campaign management data). The extreme scale of “big data”, but with the feel and semantics of “small data”.

Data 94

Data Analytics Storage Big Data 94

O'Reilly Media - Data

MAY 3, 2018

Once we have data securely in place, we proceed to utilize it in two main ways: (1) to make better decisions (BI) and (2) to enable some form of automation (ML). Business intelligence and analytics. Apple described their system in a detailed blog post : Our system is designed to be opt-in and transparent.

Analytics 125

Analytics Artificial Inteligence Data Machine Learning 125

Altexsoft

OCTOBER 21, 2022

Apache Kafka is an open-source, distributed streaming platform for messaging, storing, processing, and integrating large data volumes in real time. It offers high throughput, low latency, and scalability that meets the requirements of Big Data. Cloudera , focusing on Big Data analytics. Kafka topic and partition.

Weak Development Team 93

Weak Development Team Technical Review Systems Review Software Review 93

Altexsoft

MAY 20, 2020

These hardware components cache and preprocess real-time data, reducing the burden on central storages and main processors. The transport layer is responsible for smooth and secure data transmission from a perception to processing layer. You can create rules for different apps depending on the data type.

IoT 141

IoT Azure AWS Transportation 141

Datavail

MARCH 10, 2022

According to Gartner, only 20 percent of analytics insights will deliver business results through 2022. In other words, 80 percent of companies’ Big Data projects will fail and/or not deliver results. There are many reasons for this failure, but poor (or a complete lack of) data governance strategies is most often to blame.

Government 40

Government Strategy Weak Development Team Company 40

Datavail

AUGUST 6, 2020

The use of business analytics is a critical component of organizations’ digital transformation initiatives. As big data continues to grow in size and complexity year after year, organizations need to efficiently cut through the massive data volumes they have on hand to find the hidden insights within.

Analytics 52

Analytics Cloud Disaster Recovery Business Analytics 52

Datavail

JANUARY 13, 2020

Amazon Redshift is among the best solutions to consider for cost-effectively creating a cloud-based data warehouse. Redshift is a fully-managed big data warehousing product from Amazon Web Services (AWS), built specifically to cost-effectively collect and store up to one petabyte of data in the cloud. Cost Efficiency.

Data 10

Data AWS Big Data Storage 10

Datavail

JULY 31, 2019

Big data software companies that used to run their applications on Hadoop are now switching to Kubernetes. What’s behind the recent move from Hadoop to Kubernetes, and where is the big data landscape going in the future? Platforms like Hadoop were created during and for a different era in big data.

Development 8

Development Big Data Open Source Google Cloud 8