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

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

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 Algorithm Certification SQL 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. Optimized access to both full fidelity raw data and aggregations. Optimized access to both current data and historical data. Spark Streaming.

Data Warehouse 94

Data Warehouse Kafka Lambda Architecture Telecommunication 94

Rockset

MAY 17, 2022

Complex SQL queries have long been commonplace in business intelligence (BI). More application code not only takes more time to create, but it almost always results in slower queries. Limitations of NoSQL SQL supports complex queries because it is a very expressive, mature language.

SQL 52

SQL NoSQL Hadoop MongoDB 52

AltexSoft

SEPTEMBER 23, 2021

A data hub serves as a single point of access for all data consumers, whether it be an application, a data scientist, or a business user. 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. Data access layer: data querying.

Architecture 52

Architecture Data Lake Unstructured Data Data Warehouse 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 Data Warehouse 52

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 70

Data Warehouse Cloud Kafka Cloud Storage 70

Cloudera

JUNE 16, 2022

For governance and security teams, the questions revolve around chain of custody, audit, metadata, access control, and lineage. Moving beyond traditional data-at-rest analytics: next generation stream processing with Apache Flink. Conclusion. As Laila so accurately put it, “without context, streaming data is useless.”

Kafka 86

Kafka Manufacturing Data Lake SQL 86

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. Because metadata is always associated with your data, you can open up self-service access to more diverse users and apps without those apps becoming data silos in cloud.

Cloud 40

Cloud Metadata Big Data AWS 40

Knowledge Hut

JUNE 26, 2023

Top Data Engineering Projects with Source Code Data engineers make unprocessed data accessible and functional for other data professionals. Use Stack Overflow Data for Analytic Purposes Project Overview: What if you had access to all or most of the public repos on GitHub? Which queries do you have?

Data Engineering 98

Data Engineering Data Engineer Coding Project 98

Rockset

OCTOBER 4, 2022

Streaming data feeds many real-time analytics applications, from logistics tracking to real-time personalization. The broad adoption of Apache Kafka has helped make these event streams more accessible. ClickHouse recommends bi-level sharding to limit the impact of these small queries.

MySQL 52

MySQL Kafka Aggregated Data Architecture 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. Best of all, SDX is part of Cloudera’s core platform, not an expensive add-on. You can read here about how to deploy SDX in your cloud.

Building 40

Building Management Government BI 40

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. When it’s time to run that query, the data is fetched from S3.

Data Lake 40

Data Lake Business Intelligence SQL Datasets 40

Cloudera

MAY 16, 2018

Not moving data mitigates data loss, ensuring data integrity and if the platform security of the data lake is inherited, then the data will only be viewed by those with proper access. Conclusion.

Data Lake 42

Data Lake Electronics Media Unstructured Data 42

U-Next

AUGUST 26, 2022

Companies may use Data Analytics to evaluate their data (historic, genuine, unorganized, subjective), find connections, and provide insight that can guide and, in some circumstances, automate choices, bridging knowledge and actions. Data Analytics Illustration. Analytical Diagnostics.

Data Analytics 40

Data Analytics Insurance Transportation Healthcare 40

AltexSoft

OCTOBER 21, 2022

Another security measure is an audit log to track access. 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. Large user community. However, these systems serve different use cases.

Kafka 93

Kafka Hadoop ETL Tools Big Data 93

ProjectPro

MAY 20, 2015

Professionals aspiring to earn high-paid big data jobs must have a look at these top 6 big data companies to work for in 2015: 1) InsightSquared, Cambridge, MA InsightSquared a big data analytics company experiencing triple digit annual growth in revenues, employees and customers. “It’s not a “butt in seat” culture.

Big Data 40

Big Data Hadoop Business Analyst Unstructured Data 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. Get FREE Access to Data Analytics Example Codes for Data Cleaning, Data Munging, and Data Visualization Also Read: Data Analyst vs Data Scientist How to Become a Computer Vision Engineer in 2021?

Portfolio 40

Portfolio Data Finance Certification 40

phData: Data Engineering

JULY 18, 2023

Imagine a world where all of your data is organized, easily accessible, and routinely leveraged to drive impactful outcomes. Demands on the cloud data warehouse are also evolving to require it to become more of an all-in-one platform for an organization’s analytics needs. Read more here.

Data Warehouse 52

Data Warehouse Pipeline-centric Government Data 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. Access Solution to Data Warehouse Design for an E-com Site 4.

Big Data 52

Big Data Coding Project Hadoop 52