Data Engineering Podcast

MAY 29, 2022

report having current investments in automation, 85% of data teams plan on investing in automation in the next 12 months. The Ascend Data Automation Cloud provides a unified platform for data ingestion, transformation, orchestration, and observability. In fact, while only 3.5% That’s where our friends at Ascend.io

Database 100

Database Architecture Data Architecture PostgreSQL 100

Striim

SEPTEMBER 11, 2024

Data Collection/Ingestion The next component in the data pipeline is the ingestion layer, which is responsible for collecting and bringing data into the pipeline. By efficiently handling data ingestion, this component sets the stage for effective data processing and analysis.

Data Pipeline 98

Data Pipeline Designing Data Lake Data Warehouse 98

DareData

JANUARY 30, 2023

Our goal is to help data scientists better manage their models deployments or work more effectively with their data engineering counterparts, ensuring their models are deployed and maintained in a robust and reliable way. DigDag: An open-source orchestrator for data engineering workflows. Stanford's Relational Databases and SQL.

Data Engineering 52

Data Engineering Data Engineer NoSQL Engineering 52

Knowledge Hut

APRIL 23, 2024

It is designed to support business intelligence (BI) and reporting activities, providing a consolidated and consistent view of enterprise data. Data warehouses are typically built using traditional relational database systems, employing techniques like Extract, Transform, Load (ETL) to integrate and organize data.

Data Warehouse 52

Data Warehouse Big Data Unstructured Data Hadoop 52

ProjectPro

OCTOBER 28, 2015

Apache Hadoop is synonymous with big data for its cost-effectiveness and its attribute of scalability for processing petabytes of data. Data analysis using hadoop is just half the battle won. Getting data into the Hadoop cluster plays a critical role in any big data deployment. then you are on the right page.

ETL Tools 52

ETL Tools Hadoop Relational Database Unstructured Data 52

Databand.ai

AUGUST 30, 2023

DataOps is a collaborative approach to data management that combines the agility of DevOps with the power of data analytics. It aims to streamline data ingestion, processing, and analytics by automating and integrating various data workflows.

Architecture 52

Architecture Data Ingestion Data Governance Data Cleanse 52

Booking.com Engineering

DECEMBER 2, 2022

From data ingestion, data science, to our ad bidding[2], GCP is an accelerant in our development cycle, sometimes reducing time-to-market from months to weeks. Data Ingestion and Analytics at Scale Ingestion of performance data, whether generated by a search provider or internally, is a key input for our algorithms.

Systems 52

Systems Cloud MySQL Relational Database 52

Cloudera

JUNE 16, 2022

Faster data ingestion: streaming ingestion pipelines. Without context, streaming data is useless.” ” SSB enables users to configure data providers using out of the box connectors or their own connector to any data source.

Kafka 88

Kafka Manufacturing Data Lake SQL 88

Towards Data Science

MARCH 5, 2024

This comes with the advantages of reduction of redundancy, data integrity and consequently, less storage usage. Photo by Shubham Dhage on Unsplash While data normalization holds merit in traditional relational databases, the paradigm shifts when dealing with modern analytics platforms like BigQuery.

Bytes 97

Bytes Google Cloud Cloud Storage Utilities 97

Edureka

FEBRUARY 9, 2023

As the demand for data engineers grows, having a well-written resume that stands out from the crowd is critical. Azure data engineers are essential in the design, implementation, and upkeep of cloud-based data solutions. It is also crucial to have experience with data ingestion and transformation.

Data Engineering 52

Data Engineering Data Engineer Engineering Amazon Web Services 52

Confluent

OCTOBER 10, 2019

PLC4X: As an Apache framework, it provides a unified API by implementing drivers (similar to JDBC for relational databases) for communicating with most industrial controllers in the protocols they natively understand. MQTT Proxy for data ingestion without an MQTT broker.

Kafka 20

Kafka Google Cloud Architecture Machine Learning 20

Cloudera

AUGUST 31, 2021

Case Study: Accenture’s Experience on Legacy Data Warehouse Migration into Cloudera with a Health Insurance Company . Business Problem & Background.

Data Warehouse 82

Data Warehouse Database-centric Metadata Cloud 82

Knowledge Hut

MARCH 19, 2024

Lambda usage includes real-time data processing, communication with IoT devices, and execution of automated tasks. Amazon RDS (Relational Database Service) Another famous AWS web application is the Amazon RDS, a relational database service managed and simple to install, operate, and scale databases on the cloud.

AWS 52

AWS Cloud Computing Amazon Web Services Relational Database 52

AltexSoft

MAY 14, 2021

And most of this data has to be handled in real-time or near real-time. Variety is the vector showing the diversity of Big Data. This data isn’t just about structured data that resides within relational databases as rows and columns. Big Data analytics processes and tools. Data ingestion.

Big Data 78

Big Data Data Analytics IT NoSQL 78

phData: Data Engineering

SEPTEMBER 19, 2023

Data warehouses have their own data modeling approaches that are typically more rigid than those for a data lake. Real-time Data Ingestion and Processing Data lakes can handle real-time data streams, making them ideal for use cases that require immediate data ingestion and processing.

Data Lake 52

Data Lake Process Metadata Data Warehouse 52

ProjectPro

FEBRUARY 8, 2023

This serverless data integration service can automatically and quickly discover structured or unstructured enterprise data when stored in data lakes in Amazon S3, data warehouses in Amazon Redshift, and other databases that are a component of the Amazon Relational Database Service.

AWS 98

AWS Scala Metadata Data Lake 98

AltexSoft

JUNE 26, 2023

Read our article on Hotel Data Management to have a full picture of what information can be collected to boost revenue and customer satisfaction in hospitality. While all three are about data acquisition, they have distinct differences. Non-relational databases , on the other hand, work for data forms and structures other than tables.

Data Collection 52

Data Collection Machine Learning Unstructured Data Non-relational Database 52

ProjectPro

APRIL 1, 2016

NetworkAsia.net Hadoop is emerging as the framework of choice while dealing with big data. It can no longer be classified as a specialized skill, rather it has to become the enterprise data hub of choice and relational database to deliver on its promise of being the go to technology for Big Data Analytics.

Hadoop 52

Hadoop BI Big Data Big Data Tools 52

Silectis

JANUARY 3, 2021

Data Engineering Data engineering is a process by which data engineers make data useful. Data engineers design, build, and maintain data pipelines that transform data from a raw state to a useful one, ready for analysis or data science modeling.

Data Engineering 40

Data Engineering Data Engineer Engineering Hadoop 40

AltexSoft

AUGUST 29, 2023

Data sources In a data lake architecture, the data journey starts at the source. Data sources can be broadly classified into three categories. Structured data sources. These are the most organized forms of data, often originating from relational databases and tables where the structure is clearly defined.

Data Lake 52

Data Lake Architecture IT Amazon Web Services 52

ProjectPro

JANUARY 31, 2023

Big Data is a collection of large and complex semi-structured and unstructured data sets that have the potential to deliver actionable insights using traditional data management tools. Big data operations require specialized tools and techniques since a relational database cannot manage such a large amount of data.

Big Data 52

Big Data Hadoop Relational Database AWS 52

Rockset

JULY 6, 2022

Typically stored in SQL statements, the schema also defines all the tables in the database and their relationship to each other. Like other NoSQL databases, Rockset is highly scalable, flexible and fast at writing data. It's not true and is just one of many outdated data myths that modern offerings such as Rockset are busting.

NoSQL 52

NoSQL SQL Systems PostgreSQL 52

Monte Carlo

JANUARY 5, 2024

This architecture format consists of several key layers that are essential to helping an organization run fast analytics on structured and unstructured data. This starts at the data source.

Architecture 52

Architecture Data Lake Metadata Unstructured Data 52

Monte Carlo

JANUARY 5, 2024

This architecture format consists of several key layers that are essential to helping an organization run fast analytics on structured and unstructured data. This starts at the data source.

Architecture 52

Architecture Data Lake Metadata Unstructured Data 52

Rockset

JULY 7, 2021

This is important because it’s often helpful to include fields from multiple Druid files — or multiple tables in a normalized data set — in a single query, providing the equivalent of an SQL join in a relational database. Join Operators Join operators connect two or more datasources such as data files and Druid tables.

Database 40

Database Relational Database SQL MySQL 40

Ascend.io

JANUARY 2, 2024

These are the interfaces where the pipeline taps into various systems to acquire data. The sources of data can be incredibly diverse, ranging from data warehouses, relational databases, and web analytics to CRM platforms, social media tools, and IoT device sensors.

Data Pipeline 52

Data Pipeline Building Raw Data Data Warehouse 52

Snowflake

JANUARY 18, 2023

But legacy systems and data silos prevent easy and secure data sharing. Snowflake can help life sciences companies query and analyze data easily, efficiently, and securely. APPENDIX – Sample Functions for VCF File Data Ingestion: -- Copyright (c) 2022 Snowflake Inc. import java.util.*;

Pharmaceutical 98

Pharmaceutical AWS Java Healthcare 98

Knowledge Hut

SEPTEMBER 26, 2023

Supports Structured and Unstructured Data: One of Azure Synapse's standout features is its versatility in handling a wide array of data types. Whether your data is structured, like traditional relational databases, or unstructured, such as textual data, images, or log files, Azure Synapse can manage it effectively.

Data Lake 52

Data Lake Database-centric Machine Learning Pipeline-centric 52

ProjectPro

FEBRUARY 21, 2023

Proficiency in data ingestion, including the ability to import and export data between your cluster and external relational database management systems and ingest real-time and near-real-time (NRT) streaming data into HDFS.

Certification 52

Certification Data Engineering Data Engineer Engineering 52

Knowledge Hut

OCTOBER 3, 2023

Additionally, for a job in data engineering, candidates should have actual experience with distributed systems, data pipelines, and related database concepts. Conclusion A position that fits perfectly in the current industry scenario is Microsoft Certified Azure Data Engineer Associate.

Data Engineering 52

Data Engineering Data Engineer Engineering Cloud Computing 52

Edureka

JULY 16, 2024

Amazon AWS SageMaker runs on the workflow pipeline’s efficient functionality, including data preprocessing, model building, training, and deployment. SageMaker Ground Truth helps in data labeling by providing human labeling and active learning that enhances accuracy and reduces cost.

AWS 52

AWS Algorithm Machine Learning Amazon Web Services 52

AltexSoft

MAY 12, 2023

Structured data is formatted in tables, rows, and columns, following a well-defined, fixed schema with specific data types, relationships, and rules. A fixed schema means the structure and organization of the data are predetermined and consistent. The process requires extracting data from diverse sources, typically via APIs.

Unstructured Data 59

Unstructured Data NoSQL Hadoop Data Lake 59

Netflix Tech

DECEMBER 14, 2018

MDVS also serves as the storehouse and the manager for the data schema itself. As was noted in the previous post , data schema could itself evolve over time, but all the data, ingested hitherto, has to remain compliant with the latest schema.

Media 97

Media Database Metadata Data Schemas 97

ProjectPro

NOVEMBER 15, 2021

DataFrames are used by Spark SQL to accommodate structured and semi-structured data. You can also access data through non-relational databases such as Apache Cassandra, Apache HBase, Apache Hive, and others like the Hadoop Distributed File System. To learn more about the recent updates and contribute: [link] 8.

Big Data 52

Big Data Project Metadata Programming Language 52

Knowledge Hut

SEPTEMBER 29, 2023

You can browse the data lake files with the interactive training material. Additionally, Apache Spark can be used to learn ingestion methods. You can then use data transformation technologies once you have mastered data ingestion procedures.

Certification 52

Certification Data Engineering Data Engineer Engineering 52

Ascend.io

JANUARY 3, 2023

It has evolved over the years as data thought leaders have tackled problems like big data, data lakes, accessibility, and other modern data challenges. The Emergence of the Database The advent of the relational database system brought us fast and flexible access to our data.

Data 52

Data Data Lake Business Intelligence Big Data 52

ProjectPro

JANUARY 25, 2022

Easy Processing- PySpark enables us to process data rapidly, around 100 times quicker in memory and ten times faster on storage. When it comes to data ingestion pipelines, PySpark has a lot of advantages. PySpark allows you to process data from Hadoop HDFS , AWS S3, and various other file systems.

Big Data 52

Big Data Data Process Process Kafka 52