Data Engineering Podcast

AUGUST 28, 2022

Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode.

Data Engineering 100

Data Engineering Data Engineer MongoDB Metadata 100

U-Next

MARCH 1, 2023

A good Data Engineer will also have experience working with NoSQL solutions such as MongoDB or Cassandra, while knowledge of Hadoop or Spark would be beneficial. In 2022, data engineering will hold a share of 29.8% Being a hybrid role, Data Engineer requires technical as well as business skills.

Data Engineering 75

Data Engineering Data Engineer Non-relational Database Engineering 75

Data Engineering Podcast

MAY 15, 2022

In this episode Mário Pereira shares the system design that he and his team have developed for collecting and managing the collection and analysis of sensor data, and how they have split the data processing and business logic responsibilities between physical terminals and edge locations, and centralized storage and compute.

Designing 100

Designing MongoDB AWS SQL 100

Knowledge Hut

MAY 23, 2024

It uses batch processing to handle this flow of enormous data streams (that are unbounded - i.e., they do not have a fixed start and endpoint) as well as stored datasets (that are bounded). Python: Python is, by far, the most widely used data science programming language. Big Data Tools 23.

Data Science 52

Data Science MongoDB Programming Language Hadoop 52

Knowledge Hut

DECEMBER 21, 2023

To boost database performance, data engineers also update old systems with newer or improved versions of current technology. As a data engineer, a strong understanding of programming, databases, and data processing is necessary. Understanding of Big Data technologies such as Hadoop, Spark, and Kafka.

Data Engineering 52

Data Engineering Data Engineer Engineering MongoDB 52

Edureka

JULY 12, 2024

The MERN Stack is a popular technology stack with MongoDB as the database, Express as the web framework, and React as the javascript frame: js, React, and Node. It combines four essential technologies: MongoDB, Expres.js, React, and Node. MongoDB is software that stores data in flexible documents and is in the Non-SQL category.

MongoDB 40

MongoDB MySQL PostgreSQL NoSQL 40

Knowledge Hut

JULY 26, 2023

MongoDB): MongoDB is a prominent database software that comes under the category of "document store" databases. Document store databases, such as MongoDB, are intended to store and manage data that is unstructured or semi-structured, such as documents. Database Software- Document Store (e.g.-MongoDB):

Database 52

Database NoSQL Telecommunication MongoDB 52

Rockset

JULY 21, 2022

This also meant that we were suddenly managing a whole load of data processing pipelines, which came with all the headaches you would expect – if a scheduled data processing was missed, for example, then the user would see out-of-date data or even a chart with a chunk of data missing in the middle.

NoSQL 52

NoSQL IT MongoDB SQL 52

Striim

OCTOBER 11, 2024

Striim supported American Airlines by implementing a comprehensive data pipeline solution to modernize and accelerate operations. To achieve this, the TechOps team implemented a real-time data hub using MongoDB, Striim, Azure, and Databricks to maintain seamless, large-scale operations.

Data Pipeline 52

Data Pipeline MongoDB Unstructured Data Data Lake 52

ProjectPro

MARCH 19, 2015

Big Data NoSQL databases were pioneered by top internet companies like Amazon, Google, LinkedIn and Facebook to overcome the drawbacks of RDBMS. RDBMS is not always the best solution for all situations as it cannot meet the increasing growth of unstructured data.

NoSQL 49

NoSQL Big Data SQL Database-centric 49

Cloud Academy

JANUARY 27, 2022

Microsoft SQL Server Document-oriented database: MongoDB (classified as NoSQL) The Basics of Data Management, Data Manipulation and Data Modeling This learning path focuses on common data formats and interfaces. Apache Kafka Amazon MSK and Kafka Under the Hood Apache Kafka is an open-source streaming platform.

Certification 52

Certification Data Engineering Data Engineer Engineering 52

Knowledge Hut

JANUARY 18, 2024

For an organization, full-stack data science merges the concept of data mining with decision-making, data storage, and revenue generation. It also helps organizations to maintain complex data processing systems with machine learning.

Data Science 96

Data Science Computer Science Programming Language Machine Learning 96

AltexSoft

MAY 12, 2023

Without a fixed schema, the data can vary in structure and organization. File systems, data lakes, and Big Data processing frameworks like Hadoop and Spark are often utilized for managing and analyzing unstructured data. There are several widely used unstructured data storage solutions such as data lakes (e.g.,

Unstructured Data 59

Unstructured Data NoSQL Hadoop Data Lake 59

Striim

JUNE 11, 2024

It is the quintessential solution for safeguarding FinServ transactions as it stands out by seamlessly integrating predictive analytics with real-time data processing. By selectively capturing and replicating changes made to source data, Striim minimizes latency and resource utilization.

MongoDB 52

MongoDB MySQL Utilities Algorithm 52

Knowledge Hut

MARCH 27, 2024

Big data tools are used to perform predictive modeling, statistical algorithms and even what-if analyses. Some important big data processing platforms are: Microsoft Azure. Why Is Big Data Analytics Important? Some open-source technology for big data analytics are : Hadoop. Apache Spark. Apache Storm. Apache SAMOA.

Big Data 52

Big Data Data Analytics MongoDB Big Data Tools 52

Knowledge Hut

MARCH 7, 2024

Spark - Spark is a powerful open-source data processing tool that helps users to easily and efficiently process data. MongoDB - MongoDB is a highly effective document-oriented database system. It includes an index-based search feature that speeds up and simplifies data retrieval.

Big Data 52

Big Data Data Analytics Data Mining MongoDB 52

Knowledge Hut

APRIL 25, 2023

Database management: Data engineers should be proficient in storing and managing data and working with different databases, including relational and NoSQL databases. Data modeling: Data engineers should be able to design and develop data models that help represent complex data structures effectively.

Data Engineering 52

Data Engineering Data Engineer Engineering Google Cloud 52

Edureka

JUNE 13, 2024

In other words, it acted as an input data source, taking much of the work on data processing and transferring within Power BI. Power Query will automatically execute Query Folding under the following conditions: A data source is an object that can process query requests, just like a database used in most cases.

BI 81

BI Raw Data SQL Database 81

Rockset

OCTOBER 30, 2018

If a data processing task that takes 100 minutes on a single CPU could be reconfigured to run in parallel on 100 CPUs in 1 minute, then the price of computing this task would remain the same, but the speedup would be tremendous! The next iteration of data processing software will exploit the fluid nature of hardware in the cloud.

Cloud 40

Cloud IT MongoDB Hadoop 40

AltexSoft

MAY 14, 2021

There are also client layers where all data management activities happen. When data is in place, it needs to be converted into the most digestible forms to get actionable results on analytical queries. For that purpose, different data processing options exist. This, in turn, makes it possible to process data in parallel.

Big Data 78

Big Data Data Analytics IT NoSQL 78

Edureka

OCTOBER 8, 2024

Database Management: Storing, retrieving data, and managing it effectively are vital. Full Stack Developers are adept at working with databases, whether they are SQL-based like MySQL or No SQL like MongoDB. These store data in a more scalable and unstructured format. These skills help in effective storage and retrieval of data.

MongoDB 40

MongoDB NoSQL MySQL PostgreSQL 40

Edureka

MARCH 5, 2024

Data processing, business logic implementation, and server-side routing are some of the tasks that backend developers handle. Database Management : Designing, implementing, and managing databases to store and retrieve data effectively. The MERN stack comprises MongoDB, Express.js, React.js, and Node.js.

MongoDB 40

MongoDB PostgreSQL MySQL Java 40

Knowledge Hut

SEPTEMBER 6, 2023

Once the data is tailored to your requirements, it then should be stored in a warehouse system, where it can be easily used by applying queries. Some of the most popular database management tools in the industry are NoSql, MongoDB and oracle. You will become accustomed to challenges that you will face in the industry.

Data Science 97

Data Science Certification Business Analyst Machine Learning 97

Data Engineering Weekly

JULY 30, 2023

Learn more and sign up to join the Great Data Debate on August 16 → Airbnb: Riverbed - Optimizing Data Access at Airbnb’s Scale Lambda and Kappa are two real-time data processing architectures. The author demonstrates the same, comparing DuckDB with other industry-leading data processing frameworks.

Data Engineering 52

Data Engineering Data Engineer Engineering Kafka 52

Rockset

FEBRUARY 6, 2019

Aggregator Leaf Tailer (ALT) is the data architecture favored by web-scale companies, like Facebook, LinkedIn, and Google, for its efficiency and scalability. In this blog post, I will describe the Aggregator Leaf Tailer architecture and its advantages for low-latency data processing and analytics. We chose ALT for Rockset.

Lambda Architecture 40

Lambda Architecture Architecture MongoDB Kafka 40

Knowledge Hut

NOVEMBER 7, 2023

Amazon Web Services offers on-demand cloud computing services like storage and data processing. Data storage, management, and access skills are also required. While SQL is well-known, other notable ones include Hadoop and MongoDB. Google argues that its services are less expensive and more cost-effective than competitors.

Cloud Computing 98

Cloud Computing Cloud Amazon Web Services Entertainment 98

Edureka

AUGUST 23, 2024

For example, processed data can be stored in Amazon S3 for archival and batch processing, loaded into Amazon Redshift for data warehousing and complex queries, or indexed in Amazon Elasticsearch Service for full-text search and analytics. This supplies data to the applications waiting to use it.

AWS 52

AWS Kafka Amazon Web Services Medical 52

Knowledge Hut

DECEMBER 21, 2023

Different databases have different patterns of data storage. For instance, MongoDB stores data in a semi-structured pattern, Cassandra stores data in the form of columns, and Redis stores data as key-value pairs. Some databases like MongoDB have weak backup ability. It is also horizontally scalable.

Hadoop 52

Hadoop Big Data NoSQL Unstructured Data 52

Knowledge Hut

DECEMBER 26, 2023

In other words, they develop, maintain, and test Big Data solutions. They use technologies like Storm or Spark, HDFS, MapReduce, Query Tools like Pig, Hive, and Impala, and NoSQL Databases like MongoDB, Cassandra, and HBase. To become a Big Data Engineer, knowledge of Algorithms and Distributed Computing is also desirable.

Data Science 97

Data Science BI Machine Learning Business Intelligence 97

Knowledge Hut

APRIL 18, 2023

is particularly popular among companies requiring real-time data processing, such as chat applications, gaming platforms, and streaming services, as it provides a highly scalable, event-driven architecture that can handle many simultaneous connections without blocking. Data Handling Node.js

Architecture 97

Architecture Database Coding Programming 97

Zalando Engineering

JUNE 6, 2016

Big Data Engineers develop, maintain, test, and evaluate big data solutions, on top of building large-scale data processing systems. They’re proficient in Hadoop-based technologies such as MongoDB, MapReduce, and Cassandra, while frequently working with NoSQL databases.

Big Data 40

Big Data Programming Language MongoDB NoSQL 40

Rockset

MAY 12, 2022

It provides instant views of the real-time data. The serving layer — often MongoDB , Elasticsearch or Cassandra — then delivers those results to both dashboards and users’ ad hoc queries. Developers and data scientists also have little control over the streaming and batch data pipelines.

Analytics Application 52

Analytics Application Lambda Architecture Hadoop Database 52

Edureka

FEBRUARY 9, 2023

Data engineers don’t just work with traditional data; they’re frequently tasked with handling massive amounts of data. A data engineer should be familiar with popular Big Data tools and technologies such as Hadoop, MongoDB, and Kafka.

Data Engineering 52

Data Engineering Data Engineer Engineering Data Mining 52

Rockset

MAY 17, 2022

The tradeoff of these first-generation SQL-based big data systems was that they boosted data processing throughput at the expense of higher query latency. Hive implemented an SQL layer on Hadoop’s native MapReduce programming paradigm. As a result, the use cases remained firmly in batch mode.

SQL 52

SQL NoSQL Hadoop MongoDB 52

Ascend.io

SEPTEMBER 14, 2023

PySpark, for instance, optimizes distributed data operations across clusters, ensuring faster data processing. Use Case: Transforming monthly sales data to weekly averages import dask.dataframe as dd data = dd.read_csv('large_dataset.csv') mean_values = data.groupby('category').mean().compute()

Data Engineering 52

Data Engineering Data Engineer Python Engineering 52

Knowledge Hut

NOVEMBER 17, 2023

Understanding data modeling concepts like entity-relationship diagrams, data normalization, and data integrity is a requirement for an Azure Data Engineer. You ought to be able to create a data model that is performance- and scalability-optimized. The certification cost is $165 USD.

Data Engineering 52

Data Engineering Data Engineer Engineering Scala 52

Knowledge Hut

DECEMBER 27, 2023

Big data analytics helps companies to identify customer related trends and patterns, analyze customer behavior thus helping businesses to find ways to satisfy and retain customers and fetch new ones. Pros : Highly scalable, provides fast access to data and is useful for R&D purposes. Offers flexibility and faster data processing.

Big Data Tools 59

Big Data Tools Big Data Hadoop Database-centric 59