Data Engineering Podcast

AUGUST 27, 2021

In this episode Dan DeMers, Cinchy’s CEO, explains how their concept of a "Dataware" platform eliminates the need for costly and error prone integration processes and the benefits that it can provide for transactional and analytical application design. No more scripts, just SQL. No more scripts, just SQL.

Data Integration 100

Data Integration Architecture Data Warehouse Data Lake 100

Cloudera

FEBRUARY 8, 2024

Materialized views are valuable for accelerating common classes of business intelligence (BI) queries that consist of joins, group-bys and aggregate functions. Such a query pattern is quite common in BI queries. Apache Iceberg forms the core foundation for Cloudera’s Open Data Lakehouse with the Cloudera Data Platform (CDP).

Metadata 89

Metadata Data Warehouse BI AWS 89

Rockset

JUNE 21, 2022

We’re excited to announce that Rockset’s new connector with Snowflake is now available and can increase cost efficiencies for customers building real-time analytics applications. What’s Needed for Real-Time Analytics? These real-time, user-facing applications include personalization , gamification or in-app analytics.

Kafka 52

Kafka Data Warehouse BI Analytics Application 52

Rockset

FEBRUARY 2, 2022

Let’s explore five ways to run MongoDB analytics, along with the pros and cons of each method. 1 – Query MongoDB Directly The first and most direct approach is to run your analytical queries directly against MongoDB. There are quite a few of these on the market, with each trying to enable business intelligence (BI) on MongoDB.

MongoDB 52

MongoDB NoSQL Data Warehouse BI 52

U-Next

AUGUST 23, 2022

A rapidly expanding data visualization tool called Tableau Software is creating a stir within Business Intelligence (BI) sector. What distinguishes Tableau and Power BI from one another? In contrast, Power BI’s high degree of integration will enable us to create effective dashboards from an executive perspective.

BI 52

BI Amazon Web Services Database Business Intelligence 52

Towards Data Science

APRIL 26, 2023

As data practitioners we want (and love) to build applications on top of our data as seamlessly as possible. Whether you work in BI, Data Science or ML all that matters is the final application and how fast you can see it working end-to-end. A lightinign fast analytics app built with our system. Image from the authors.

Engineering 67

Engineering Data Lake AWS BI 67

Rockset

MAY 17, 2022

Complex SQL queries have long been commonplace in business intelligence (BI). Or a market news provider that needs to monitor and ensure that its financial customers are getting accurate, relevant updates within the narrow windows for profitable trades. Hive implemented an SQL layer on Hadoop’s native MapReduce programming paradigm.

SQL 52

SQL NoSQL Hadoop MongoDB 52

Cloudera

NOVEMBER 2, 2020

It has the key elements of fast ingest, fast storage, and immediate querying for BI purposes. These include stream processing/analytics, batch processing, tiered storage (i.e. Analytics storage engine for huge volumes of fast arriving data. Interactive query engine for fast BI over huge volumes of data. Spark Streaming.

Data Warehouse 95

Data Warehouse Kafka Lambda Architecture Telecommunication 95

Rockset

AUGUST 18, 2020

In the world of batch analytics and BI dashboards you knew exactly what data you needed and what type of questions your business was asking - which gave you the luxury of setting up all sorts of pipelines to format the data exactly as needed, create materialized views, optimize the queries.

Data Lake 40

Data Lake Data Schemas BI Kafka 40

Cloudera

FEBRUARY 9, 2021

A typical approach that we have seen in customers’ environments is that ETL applications pull data with a frequency of minutes and land it into HDFS storage as an extra Hive table partition file. In this way, the analytic applications are able to turn the latest data into instant business insights. Design Detail.

Data Warehouse 72

Data Warehouse Cloud Kafka Cloud Storage 72

Knowledge Hut

MARCH 15, 2024

You will acquire the skills necessary to combine, process, and organize data from different structured and unstructured data sources into frameworks appropriate for constructing analytics applications that make use of Microsoft Azure data services.

Cloud Computing 52

Cloud Computing Certification Algorithm Cloud 52

Cloudera

MARCH 6, 2018

This leads to extra cost, effort, and risk to stitch together a sub-optimal platform for multi-disciplinary, cloud-based analytics applications. Instead, they have separate data stores and inconsistent (if any) frameworks for data governance, management, and security. Further, much of the value of cloud is for elastic workloads.

Cloud 40

Cloud Metadata Big Data Analytics Application 40

Knowledge Hut

JUNE 16, 2023

Top Hiring Companies Companies such as Tata Consultancy Services, Capgemini, USEReady, Dell Technologies, Accenture, Wipro, Hewlett Packard Enterprises (HPE), IBM, Visual BI solutions, etc., are regularly hiring Business Intelligence analysts. regularly hire Data Engineers in India.

Data Analytics 52

Data Analytics Business Analyst Business Intelligence Consulting 52

Cloudera

DECEMBER 7, 2017

Now we are releasing the reference architecture for you build your own self-managed SDX foundation for all your cloud-based data and analytics applications. We announced SDX at Strata New York in September 2017. Best of all, SDX is part of Cloudera’s core platform, not an expensive add-on.

Building 40

Building Management Government BI 40

Rockset

OCTOBER 4, 2022

Streaming data feeds many real-time analytics applications, from logistics tracking to real-time personalization. Change data capture (CDC) streams from OLTP databases, which may provide sales, demographic or inventory data, are another valuable source of data for real-time analytics use cases.

MySQL 52

MySQL Kafka Aggregated Data Architecture 52

AltexSoft

SEPTEMBER 23, 2021

The main purpose of a DW is to enable analytics: It is designed to source raw historical data, apply transformations, and store it in a structured format. This type of storage is a standard part of any business intelligence (BI) system, an analytical interface where users can query data to make business decisions.

Architecture 52

Architecture Data Lake Unstructured Data Data Warehouse 52

Knowledge Hut

JUNE 26, 2023

Business intelligence solutions like as Power BI, Tableau, and Looker may assist companies in mitigating operational risk and achieving maximum efficiency in terms of operations enablement by assisting businesses in making choices that are supported by data.

Data Engineer 98

Data Engineer Data Engineering Coding Project 98

Rockset

FEBRUARY 9, 2021

Because it integrates easily with S3, is serverless, and uses a familiar language, Athena has become the default service for most business intelligence (BI) decision makers to query the large amounts of (usually streaming) data coming into their object stores.

Data Lake 40

Data Lake Business Intelligence SQL Datasets 40

Cloudera

MAY 16, 2018

Arcadia Enterprise runs within the Cloudera data platform and enables business intelligence (BI) and rich visual analytic applications to be built for hundreds of business users working on data in Hadoop.

Data Lake 41

Data Lake Electronics Media Unstructured Data 41

AltexSoft

OCTOBER 21, 2022

Hadoop fits heavy, not time-critical analytics applications that generate insights for long-term planning and strategic decisions. The Good and the Bad of Power BI Data Visualization. Hadoop works with batches while Kafka deals with streams, but all the same, the similarities are obvious. Kafka vs ETL.

Kafka 93

Kafka Hadoop Big Data ETL Tools 93

ProjectPro

MAY 20, 2015

The company targets to deliver values to its customers through the free SaaS based analytics applications so that it can build credibility with the clients to encourage them to buy more. The products and services of Cloudera are changing the economics of big data analysis , BI, data processing and warehousing through Hadooponomics.

Big Data 40

Big Data Hadoop Business Analyst Data Analytics 40

AltexSoft

JULY 18, 2023

It has in-memory computing capabilities to deliver speed, a generalized execution model to support various applications, and Java, Scala, Python, and R APIs. Spark Streaming enhances the core engine of Apache Spark by providing near-real-time processing capabilities, which are essential for developing streaming analytics applications.

Big Data 52

Big Data Data Process Process Hadoop 52

ProjectPro

APRIL 19, 2021

These reports can be in the form of PowerPoint Decks, Excel Dashboards, or a report from any visualization tool like Tableau, Power BI, or QlikView. These analytic projects will take you through everything you need to know as a data analyst and how to build and deploy an ETL pipeline to combine it with the analytics application.

Portfolio 40

Portfolio Data Certification Finance 40

U-Next

AUGUST 26, 2022

Due to this, our Data Consultants advise highly data-driven businesses not to limit themselves to descriptive analytics alone but instead to integrate it with other forms of Data Analytics. Analytical Diagnostics. At this point, the issue of “why something occurred can be answered” by comparing previous data to some other data.

Data Analytics 40

Data Analytics Insurance Transportation Healthcare 40

U-Next

AUGUST 11, 2022

BI tools are different types of application software that collect and process huge amounts of unstructured data from internal and external sources. Any business user may quickly resolve difficulties using current and skilled Business Intelligence (BI) technologies, even without intensive IT participation. Introduction.

Business Intelligence 40

Business Intelligence BI Unstructured Data Programming 40

Snowflake

AUGUST 14, 2024

It streamlines the development of intuitive, self-serve analytics applications for business users, while providing industry-leading accuracy. Historically, business users have primarily relied on BI dashboards and reports to answer their data questions.

BI 52

BI Government Analytics Application SQL 52

ProjectPro

MAY 31, 2021

A big data project is a data analysis project that uses machine learning algorithms and different data analytics techniques on a large dataset for several purposes, including predictive modeling and other advanced analytics applications. The project uses Power BI to visualize batch forecasts.

Big Data 52

Big Data Coding Project Hadoop 52

Rockset

DECEMBER 9, 2021

Disclaimer: Rockset is a real-time analytics database and one of the pieces in the modern real-time data stack So What is Real-Time Data (And Why Can’t the Modern Data Stack Handle It)? Real-time data streams typically power analytical or data applications whereas batch systems were built to power static dashboards.

Transportation 52

Transportation BI SQL Database 52

Edureka

JULY 3, 2024

This makes the data ready for consumption by BI tools, analytics applications, or other systems. Publish: Transformed data is then published either back to on-premises sources like SQL Server or kept in cloud storage. Manage Workflow: ADF manages these processes through time-sliced, scheduled pipelines.

Pipeline-centric 52

Pipeline-centric Data Lake Database-centric Data Pipeline 52

phData: Data Engineering

JULY 18, 2023

CDWs are designed for running large and complex queries across vast amounts of data, making them ideal for centralizing an organization’s analytical data for the purpose of business intelligence and data analytics applications.

Data Warehouse 52

Data Warehouse Pipeline-centric Government Data 52

Cloudera

JUNE 16, 2022

Building real-time data analytics pipelines is a complex problem, and we saw customers struggle using processing frameworks such as Apache Storm, Spark Streaming, and Kafka Streams. . SSB can materialize the results from a streaming SQL query to a persistent view of the data that can be read via a REST API.

Kafka 85

Kafka Manufacturing Data Lake SQL 85