Towards Data Science

JANUARY 16, 2024

But in order to justify why this concept came into existence, I thought it’d be great to look back in time and understand the evolution of the data landscape. Evolution of the data landscape 1980s — Inception Relational databases came into existence. Organizations began to use relational databases for ‘everything’.

Data Warehouse 66

Data Warehouse Data Architecture Relational Database NoSQL 66

Monte Carlo

JANUARY 5, 2024

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. A visualization of the flow of data in data lakehouse architecture vs. data warehouse and data lake.

Architecture 52

Architecture Data Lake Metadata Unstructured Data 52

Monte Carlo

JANUARY 5, 2024

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. A visualization of the flow of data in data lakehouse architecture vs. data warehouse and data lake.

Architecture 52

Architecture Data Lake Metadata Unstructured Data 52

Knowledge Hut

MARCH 19, 2024

It also keeps backups, media files, log data, and static website content. S3 is suitable across several scenarios that utilize S3’s durability, availability, and security features, such as data archiving, content distribution, and data lake implementations, among many others.

AWS 52

AWS Cloud Computing Amazon Web Services Relational Database 52

Knowledge Hut

NOVEMBER 17, 2023

To provide end users with a variety of ready-made models, Azure Data engineers collaborate with Azure AI services built on top of Azure Cognitive Services APIs. Understanding SQL You must be able to write and optimize SQL queries because you will be dealing with enormous datasets as an Azure Data Engineer.

Data Engineering 52

Data Engineering Data Engineer Engineering Scala 52

François Nguyen

MARCH 22, 2021

With this 3rd platform generation, you have more real time data analytics and a cost reduction because it is easier to manage this infrastructure in the cloud thanks to managed services. The data domain Discovery portal with all the metadata on the data life cycle 4.Federated The number of subjects to automatize is not short.

Architecture 130

Architecture Technology Google Cloud Metadata 130

Grouparoo

OCTOBER 6, 2021

36 Give Data Products a Frontend with Latent Documentation Document more to help everyone 37 How Data Pipelines Evolve Build ELT at mid-range and move to data lakes when you need scale 38 How to Build Your Data Platform like a Product PM your data with business. 89 What Is Big Data?

Data Engineering 52

Data Engineering Data Engineer Engineering Pipeline-centric 52

Ascend.io

JANUARY 2, 2024

Ingestion Points at the Source The journey of a data pipeline begins at its sources – or more technically, at the ingestion points. These are the interfaces where the pipeline taps into various systems to acquire data. Questions to Ask: What storage solutions (data warehouses, data lakes, etc.)

Data Pipeline 52

Data Pipeline Building Raw Data Data Warehouse 52

Silectis

JANUARY 3, 2021

Data Ingestion The process by which data is moved from one or more sources into a storage destination where it can be put into a data pipeline and transformed for later analysis or modeling. Data Integration Combining data from various, disparate sources into one unified view.

Data Engineering 40

Data Engineering Data Engineer Engineering Hadoop 40

Knowledge Hut

SEPTEMBER 26, 2023

We as Azure Data Engineers should have extensive knowledge of data modelling and ETL (extract, transform, load) procedures in addition to extensive expertise in creating and managing data pipelines, data lakes, and data warehouses. is the responsibility of data engineers.

Certification 52

Certification Data Engineering Data Engineer Engineering 52

Databand.ai

AUGUST 30, 2023

Data Storage Once data is ingested, it must be stored in a suitable data storage platform that can accommodate the volume, variety, and velocity of the data being processed. Data storage platforms can include traditional relational databases, NoSQL databases, data lakes, or cloud-based storage services.

Architecture 52

Architecture Data Ingestion Data Governance Data Cleanse 52

ProjectPro

APRIL 2, 2018

It will also discuss about how enterprises have setup data lakes to bring in information from diverse sources but are facing totally new set of challenges as users are not completely able to make use of the data because of slow query response times and data complexity.

Hadoop 40

Hadoop Data Lake Relational Database BI 40

Grouparoo

OCTOBER 26, 2021

Origin The origin of a data pipeline refers to the point of entry of data into the pipeline. This includes the different possible sources of data such as application APIs, social media, relational databases, IoT device sensors, and data lakes.

Data Pipeline 52

Data Pipeline ETL Tools ETL System Data Warehouse 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. Data lakes built on NoSQL databases such as Hadoop are the best example of scaled-out data repositories of mixed types.

NoSQL 52

NoSQL SQL Systems PostgreSQL 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

DECEMBER 7, 2021

Generally, data pipelines are created to store data in a data warehouse or data lake or provide information directly to the machine learning model development. Keeping data in data warehouses or data lakes helps companies centralize the data for several data-driven initiatives.

Data Pipeline 52

Data Pipeline Architecture Kafka AWS 52

ProjectPro

AUGUST 11, 2021

“Data Lake vs Data Warehouse = Load First, Think Later vs Think First, Load Later” The terms data lake and data warehouse are frequently stumbled upon when it comes to storing large volumes of data. Data Warehouse Architecture What is a Data lake?

Data Lake 52

Data Lake Data Warehouse Cloud Hadoop 52

U-Next

SEPTEMBER 7, 2022

The terms “ Data Warehouse ” and “ Data Lake ” may have confused you, and you have some questions. Structuring data refers to converting unstructured data into tables and defining data types and relationships based on a schema. What is Data Lake? . Athena on AWS. .

Data Lake 52

Data Lake Data Warehouse Unstructured Data Amazon Web Services 52

Knowledge Hut

SEPTEMBER 29, 2023

You can browse the data lake files with the interactive training material. You can then use data transformation technologies once you have mastered data ingestion procedures. Data Engineer: Data platform solutions that are on-premises and in the cloud are provisioned and set up by data engineers.

Certification 52

Certification Data Engineering Data Engineer Engineering 52

Knowledge Hut

OCTOBER 30, 2023

Develop a long-term vision for Power BI implementation and data analytics. Data Architecture and Design: Lead the design and development of complex data architectures, including data warehouses, data lakes, and data marts. Define data architecture standards and best practices.

BI 52

BI Business Intelligence Data Cleanse Business Analyst 52

Rockset

MAY 24, 2021

The most popular data storage layers for a serverless stack include: Amazon S3: Amazon Simple Storage Service is offered through AWS as a scalable infrastructure solution. Azure Data Lake: Microsoft's analytics platform and serverless data lake is offered through the company's public cloud, Azure.

Database 40

Database Google Cloud AWS Cloud Storage 40

ProjectPro

MAY 2, 2017

Despite the hype around NoSQL, SQL is still the go-to query language for relational databases and other emerging novel database technologies. Source : [link] ) Hadoop: the rise of the modern data lake platform.Information-age.com, April 5, 2017. Source : [link] ) Data Works, Hadoop 3.0

Hadoop 52

Hadoop Entertainment Data Lake Big Data 52

Rockset

OCTOBER 20, 2021

This can be helpful when you want to reduce the size of large scale data streams, deduplicate data, or partition your data. Collections can also be created from other data sources including data lakes (e.g. S3 or GCS), NoSQL databases (e.g. DynamoDB or MongoDB), and relational databases (e.g.

SQL 52

SQL PostgreSQL MongoDB NoSQL 52

Rockset

AUGUST 4, 2021

Security Data applications providing security and fraud detection need to react to streams of data in near real-time. You can speed up Snowpipe data ingestion by writing smaller files to your data lake. This makes the data available sooner. Next Steps Snowflake’s scalable relational database is cloud-native.

Data Ingestion 40

Data Ingestion Cloud Storage Data Warehouse Data Lake 40

AltexSoft

FEBRUARY 11, 2023

It serves as a foundation for the entire data management strategy and consists of multiple components including data pipelines; , on-premises and cloud storage facilities – data lakes , data warehouses , data hubs ;, data streaming and Big Data analytics solutions ( Hadoop , Spark , Kafka , etc.);

Data Architect 87

Data Architect Certification Generalist Big Data 87

Knowledge Hut

OCTOBER 3, 2023

Being an associate level course, you must have some prior knowledge in Power BI, Data Analysis Expressions (DAX), and Power Query. Further proficiency in visualizing data, analyzing data by using Transact-SQL (T-SQL), and querying relational databases will come handy.

Certification 52

Certification Cloud Computing Machine Learning SQL 52

Rockset

MARCH 5, 2020

Keep in mind that complex ETL processes can add minutes to hours before the data is available to query. If the use case does not require the freshest data, then it may be sufficient to use a data warehouse or data lake to store Kafka data for analysis.

Kafka 40

Kafka Data Warehouse Data Lake Relational Database 40

Knowledge Hut

NOVEMBER 28, 2023

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. Kafka is great for ETL and provides memory buffers that provide process reliability and resilience.

Data Engineering 52

Data Engineering Data Engineer Engineering Generalist 52

ProjectPro

FEBRUARY 21, 2023

Must be familiar with data architecture, data warehousing, parallel processing concepts, etc. Proficient in building data processing solutions using Azure Data Factory , Azure Synapse Analytics , Azure Data Lake Storage, Azure Databricks , etc.

Certification 52

Certification Data Engineering Data Engineer Engineering 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

ProjectPro

FEBRUARY 16, 2023

Data engineers are responsible for these data integration and ELT tasks, where the initial step requires extracting data from different types of databases/files, such as RDBMS, flat files, etc. Engineers can also use the "LOAD DATA INFILE" command to extract data from flat files like CSV or TXT.

Data Engineering 52

Data Engineering Data Engineer SQL Engineering 52

Knowledge Hut

DECEMBER 29, 2023

A Data Engineer is someone proficient in a variety of programming languages and frameworks, such as Python, SQL, Scala, Hadoop, Spark, etc. One of the primary focuses of a Data Engineer's work is on the Hadoop data lakes. NoSQL databases are often implemented as a component of data pipelines.

Data Science 98

Data Science Data Mining Deep Learning Programming Language 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 Pipeline-centric Machine Learning 52

DareData

JANUARY 30, 2023

Examples of relational databases include MySQL or Microsoft SQL Server. NoSQL databases: NoSQL databases are often used for applications that require high scalability and performance, such as real-time web applications. Examples of NoSQL databases include MongoDB or Cassandra.

Data Engineering 52

Data Engineering Data Engineer NoSQL Engineering 52

Precisely

JANUARY 25, 2024

There are three potential approaches to mainframe modernization: Data Replication creates a duplicate copy of mainframe data in a cloud data warehouse or data lake, enabling high-performance analytics virtually in real time, without negatively impacting mainframe performance.

Metadata 69

Metadata Data Governance Relational Database Government 69

Edureka

FEBRUARY 9, 2023

Some of the top skills to include are: Experience with Azure data storage solutions: Azure Data Engineers should have hands-on experience with various Azure data storage solutions such as Azure Cosmos DB, Azure Data Lake Storage, and Azure Blob Storage.

Data Engineering 52

Data Engineering Data Engineer Engineering Amazon Web Services 52

Rockset

OCTOBER 11, 2022

Logstash is an event processing pipeline that ingests and transforms data before sending it to Elasticsearch. Logstash offers a JDBC input plugin that polls a relational database, like PostgreSQL or MySQL, for inserts and updates periodically.

Data Ingestion 52

Data Ingestion Kafka Relational Database PostgreSQL 52