Monte Carlo

JANUARY 5, 2024

You know what they always say: data lakehouse architecture is like an onion. …ok, Data lakehouse architecture combines the benefits of data warehouses and data lakes, bringing together the structure and performance of a data warehouse with the flexibility of a data lake. But they should!

Architecture 52

Architecture Data Lake Metadata Unstructured Data 52

Monte Carlo

JANUARY 5, 2024

You know what they always say: data lakehouse architecture is like an onion. …ok, Data lakehouse architecture combines the benefits of data warehouses and data lakes, bringing together the structure and performance of a data warehouse with the flexibility of a data lake. But they should!

Architecture 52

Architecture Data Lake Metadata Unstructured Data 52

Striim

SEPTEMBER 11, 2024

The approach to this processing depends on the data pipeline architecture, specifically whether it employs ETL (Extract, Transform, Load) or ELT (Extract, Load, Transform) processes. This method is advantageous when dealing with structured data that requires pre-processing before storage.

Data Pipeline 98

Data Pipeline Designing Data Lake Data Warehouse 98

U-Next

MARCH 1, 2023

Introduction Data Engineer is responsible for managing the flow of data to be used to make better business decisions. A solid understanding of relational databases and SQL language is a must-have skill, as an ability to manipulate large amounts of data effectively. What is a data warehouse?

Data Engineering 75

Data Engineering Data Engineer Non-relational Database Engineering 75

Knowledge Hut

JUNE 23, 2023

You should have the expertise to collect data, conduct research, create models, and identify patterns. You should be well-versed with SQL Server, Oracle DB, MySQL, Excel, or any other data storing or processing software. You must develop predictive models to help industries and businesses make data-driven decisions.

Data Engineering 52

Data Engineering Data Engineer Engineering Non-relational Database 52

ProjectPro

JANUARY 24, 2023

With the global cloud data warehousing market likely to be worth $10.42 billion by 2026, cloud data warehousing is now more critical than ever. Cloud data warehouses offer significant benefits to organizations, including faster real-time insights, higher scalability, and lower overhead expenses.

Bytes 52

Bytes Google Cloud Data Warehouse Cloud Storage 52

Lyft Engineering

OCTOBER 3, 2023

In this post we will define data quality at a high-level and explore our motivation to achieve better data quality. We will then introduce our in-house product, Verity, and showcase how it serves as a central platform for ensuring data quality in our Hive Data Warehouse. What and Where is Data Quality?

Big Data 85

Big Data Metadata Data Warehouse Data 85

Data Engineering Podcast

JULY 16, 2021

Hightouch is the easiest way to sync data into the platforms that your business teams rely on. The data you’re looking for is already in your data warehouse and BI tools. Connect your warehouse to Hightouch, paste a SQL query, and use their visual mapper to specify how data should appear in your SaaS systems.

High Quality Data 100

High Quality Data Data Engineering Data Engineer Coding 100

Knowledge Hut

DECEMBER 26, 2023

Certain roles like Data Scientists require a good knowledge of coding compared to other roles. Data Science also requires applying Machine Learning algorithms, which is why some knowledge of programming languages like Python, SQL, R, Java, or C/C++ is also required.

Data Science 97

Data Science BI Business Intelligence Machine Learning 97

Data Science Blog: Data Engineering

DECEMBER 23, 2022

Noch konkreter wird der Bedarf an Datenbeschaffung und -aufbereitung in der Business Intelligence, denn diese benötigt für nachhaltiges Reporting feste Strukturen wie etwa ein Data Warehouse. Andere Arten von Datenbanken, sogenannte NoSQL -Datenbanken beruhen auf Dateiformaten, einer Spalten- oder einer Graphenorientiertheit.

Data Engineering 52

Data Engineering Data Engineer Engineering Business Intelligence 52

Edureka

AUGUST 29, 2024

For small companies, the data engineer holds a generalist position where he basically does all it. In big organizations, they would focus on pipeline building or play a Data Warehouse Manager. Why Choose Data Engineering as a Career? Databases: Knowledgeable about SQL and NoSQL databases.

Data Engineering 40

Data Engineering Data Engineer Engineering Generalist 40

Edureka

JUNE 13, 2024

The Query must start with something other than an SQL statement of your own. Specifically, if you begin a Google query with your SQL command, none of the steps in the Query will be rewritten. How to Find If Your Power BI Data Source Supports Query Folding? Contents, Folder.

BI 81

BI Raw Data SQL Database 81

Knowledge Hut

NOVEMBER 28, 2023

SQL Today, more and more cloud-based systems add SQL-like interfaces that allow you to use SQL. ETL is central to getting your data where you need it. Relational database management systems (RDBMS) remain the key to data discovery and reporting, regardless of their location.

Data Engineering 52

Data Engineering Data Engineer Engineering Generalist 52

Knowledge Hut

NOVEMBER 19, 2023

It makes it possible for all companies from all industries to utilize it for a variety of use cases, including data engineering, operational data integration, analytics, integrating data into data warehouses, and more. Obtaining the Data Engineer Azure certification is a great way to learn this important tool.

Data Engineering 52

Data Engineering Data Engineer Engineering PostgreSQL 52

Knowledge Hut

APRIL 25, 2023

Data engineers add meaning to the data for companies, be it by designing infrastructure or developing algorithms. The practice requires them to use a mix of various programming languages, data warehouses, and tools. While they go about it - enter big data data engineer tools.

Data Engineering 52

Data Engineering Data Engineer Engineering Google Cloud 52

Rockset

SEPTEMBER 13, 2022

DynamoDB is a NoSQL database provided by AWS. You can choose to perform periodic exports and bulk-loads of your data, but this results in stale data between loads. Alternatively, you can stream data into your data warehouse in an append-only fashion, but you can't perform in-place updates on changing data.

SQL 52

SQL Database Relational Database NoSQL 52

Monte Carlo

OCTOBER 31, 2024

Proficiency in Programming Languages Knowledge of programming languages is a must for AI data engineers and traditional data engineers alike. In addition, AI data engineers should be familiar with programming languages such as Python , Java, Scala, and more for data pipeline, data lineage, and AI model development.

Data Engineering 52

Data Engineering Data Engineer Engineering Unstructured Data 52

Cloudera

OCTOBER 13, 2020

In the beginning, CDP ran only on AWS with a set of services that supported a handful of use cases and workload types: CDP Data Warehouse: a kubernetes-based service that allows business analysts to deploy data warehouses with secure, self-service access to enterprise data. That Was Then.

Cloud 95

Cloud Data Warehouse AWS Machine Learning 95

Cloudera

AUGUST 31, 2021

Cloudera and Accenture demonstrate strength in their relationship with an accelerator called the Smart Data Transition Toolkit for migration of legacy data warehouses into Cloudera Data Platform. Accenture’s Smart Data Transition Toolkit . Are you looking for your data warehouse to support the hybrid multi-cloud?

Data Warehouse 80

Data Warehouse Database-centric Metadata Cloud 80

Ascend.io

JUNE 22, 2023

Ascend’s platform is the first modern, cloud-based data pipeline automation tool capable of ingesting data from all major platforms and delivering it to MotherDuck (relational, NoSQL, event streams/queues, APIs and custom data sources). You have tons of other options available in Ascend’s connection catalog!

NoSQL 52

NoSQL Google Cloud MySQL Data Pipeline 52

Rockset

JULY 28, 2022

MongoDB has grown from a basic JSON key-value store to one of the most popular NoSQL database solutions in use today. This can be used to create data lakes, populate data warehouses or for specific use cases like offloading analytics and text search. It also provides native querying and analytics capabilities.

MongoDB 52

MongoDB Kafka NoSQL Data Lake 52

Ascend.io

NOVEMBER 21, 2023

The emergence of cloud data warehouses, offering scalable and cost-effective data storage and processing capabilities, initiated a pivotal shift in data management methodologies. Extract The initial stage of the ELT process is the extraction of data from various source systems.

Raw Data 52

Raw Data Data Warehouse Data Cleanse Data Integration 52

AltexSoft

JUNE 26, 2023

Such tables create the basis for business intelligence, traditional data analytics, and time series forecasting (if data about the same item is collected at different points of time.) Structured data is modeled to be easily searchable and occupy minimal storage space. and its value (male, red, $100, etc.).

Data Collection 52

Data Collection Machine Learning Unstructured Data Non-relational Database 52

Monte Carlo

JUNE 2, 2024

To get a better understanding of their responsibilities, we analyzed 41 job postings on LinkedIn and found these key duties: Designing and Implementing Data Pipelines: The primary responsibility in 90% of the job postings is creating and managing data pipelines.

Engineering 52

Engineering Amazon Web Services Data Science AWS 52

Monte Carlo

JUNE 2, 2024

To get a better understanding of their responsibilities, we analyzed 41 job postings on LinkedIn and found these key duties: Designing and Implementing Data Pipelines: The primary responsibility in 90% of the job postings is creating and managing data pipelines.

Engineering 52

Engineering Amazon Web Services Data Science AWS 52

AltexSoft

NOVEMBER 10, 2021

The pun being obvious, there’s more to that than just a new term: Data lakehouses combine the best features of both data lakes and data warehouses and this post will explain this all. What is a data lakehouse? Data warehouse vs data lake vs data lakehouse: What’s the difference.

Architecture 52

Architecture Data Lake Data Warehouse Metadata 52

AltexSoft

MAY 14, 2021

With the ETL approach, data transformation happens before it gets to a target repository like a data warehouse, whereas ELT makes it possible to transform data after it’s loaded into a target system. Data storage and processing. NoSQL databases. Cassandra is an open-source NoSQL database developed by Apache.

Big Data 78

Big Data Data Analytics IT NoSQL 78

Edureka

SEPTEMBER 3, 2024

It backs up and restores relational DBMS, NoSQL, data warehouses, and any other data repository types. Currently, the service offers both structured transfers, including Oracle-Oracle or Microsoft SQL Server-Aurora, and unstructured transfers.

AWS 40

AWS Database MySQL PostgreSQL 40

ProjectPro

SEPTEMBER 9, 2021

Spark SQL, for instance, enables structured data processing with SQL. Highly flexible and scalable Real-time stream processing Spark Stream – Extension of Spark enables live-stream from massive data volumes from different web sources. Hive uses HQL, while Spark uses SQL as the language for querying the data.

Hadoop 52

Hadoop Big Data Tools Java SQL 52

ProjectPro

JULY 21, 2015

Apache Hive is an effective standard for SQL-in- Hadoop. Hive is a front end for parsing SQL statements, generating logical plans, optimizing logical plans, translating them into physical plans which are executed by MapReduce jobs. Impala is an open source SQL query engine developed after Google Dremel.

Hadoop 40

Hadoop SQL NoSQL Kafka 40

Rockset

MARCH 10, 2022

This means that updates are inserted into a different location or you’re forced to rewrite old and new data to store it properly. Immutable data stores have been useful in certain analytics scenarios. Analytical queries could be accelerated by caching heavily-accessed read-only data in RAM or SSDs.

Data Analytics 52

Data Analytics Data Warehouse Medical MySQL 52

Knowledge Hut

SEPTEMBER 25, 2023

Skill Requirements for Azure Data Engineer Job Description Here are some important skill requirements that you may find in a job description for Azure Data Engineers: 1. Azure Data Engineers work with these and other solutions. They guarantee that the data is efficiently cleaned, converted, and loaded.

Data Engineering 52

Data Engineering Data Engineer Engineering Data Lake 52

Rockset

JANUARY 28, 2022

And that was only possible if both internal and external users could drill down into the freshest data possible in order to get the answers they needed. However, Seesaw’s DynamoDB database stored the data in its own NoSQL format that made it easy to build applications, just not analytical ones.

NoSQL 52

NoSQL PostgreSQL MongoDB ETL Tools 52

Grouparoo

OCTOBER 6, 2021

This provided a nice overview of the breadth of topics that are relevant to data engineering including data warehouses/lakes, pipelines, metadata, security, compliance, quality, and working with other teams. 69 The End of ETL as We Know It Use events from the product to notify data systems of changes. Be adaptable.

Data Engineering 52

Data Engineering Data Engineer Engineering Pipeline-centric 52

AltexSoft

OCTOBER 30, 2021

Data engineer’s integral task is building and maintaining data infrastructure — the system managing the flow of data from its source to destination. This typically includes setting up two processes: an ETL pipeline , which moves data, and a data storage (typically, a data warehouse ), where it’s kept.

Data Engineering 98

Data Engineering Data Engineer Engineering Machine Learning 98

Knowledge Hut

OCTOBER 3, 2023

Candidates must, however, be proficient in programming concepts and SQL syntax prior to starting the Azure certification training. Additionally, for a job in data engineering, candidates should have actual experience with distributed systems, data pipelines, and related database concepts.

Data Engineering 52

Data Engineering Data Engineer Engineering Cloud Computing 52

AltexSoft

JULY 29, 2022

On top of HDFS, the Hadoop ecosystem provides HBase , a NoSQL database designed to host large tables, with billions of rows and millions of columns. To facilitate data ingestion, there are Apache Flume aggregating log data from multiple servers and Apache Sqoop designed to transport information between Hadoop and relational (SQL) databases.

Hadoop 59

Hadoop Big Data Google Cloud NoSQL 59