AltexSoft

AUGUST 29, 2023

In 2010, a transformative concept took root in the realm of data storage and analytics — a data lake. The term was coined by James Dixon , Back-End Java, Data, and Business Intelligence Engineer, and it started a new era in how organizations could store, manage, and analyze their data. What is a data lake?

Data Lake 52

Data Lake Architecture IT Amazon Web Services 52

Monte Carlo

NOVEMBER 21, 2024

Think of it as the “slow and steady wins the race” approach to data processing. Stream Processing Pattern Now, imagine if instead of waiting to do laundry once a week, you had a magical washing machine that could clean each piece of clothing the moment it got dirty. The data lakehouse has got you covered! ​​

Data Pipeline 52

Data Pipeline Designing Lambda Architecture Kafka 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

Towards Data Science

FEBRUARY 15, 2023

Before going into further details on Delta Lake, we need to remember the concept of Data Lake, so let’s travel through some history. In theory, was just throwing everything inside Hadoop and later on writing jobs to process the data into the expected results, getting rid of complex data warehousing systems.

Data Lake 98

Data Lake Data Warehouse Hadoop Architecture 98

U-Next

MARCH 1, 2023

Being a hybrid role, Data Engineer requires technical as well as business skills. They build scalable data processing pipelines and provide analytical insights to business users. A Data Engineer also designs, builds, integrates, and manages large-scale data processing systems.

Data Engineering 75

Data Engineering Data Engineer Non-relational Database Engineering 75

The Modern Data Company

MAY 10, 2023

In our previous post, The Pros and Cons of Leading Data Management and Storage Solutions , we untangled the differences among data lakes, data warehouses, data lakehouses, data hubs, and data operating systems. Consider whether you need a solution that supports one or multiple data formats.

Data Management 52

Data Management Management Data Lake Data Warehouse 52

The Modern Data Company

MAY 10, 2023

In our previous post, The Pros and Cons of Leading Data Management and Storage Solutions , we untangled the differences among data lakes, data warehouses, data lakehouses, data hubs, and data operating systems. Consider whether you need a solution that supports one or multiple data formats.

Data Management 52

Data Management Management Data Lake Data Warehouse 52

The Modern Data Company

MAY 10, 2023

In our previous post, The Pros and Cons of Leading Data Management and Storage Solutions , we untangled the differences among data lakes, data warehouses, data lakehouses, data hubs, and data operating systems. Consider whether you need a solution that supports one or multiple data formats.

Data Management 52

Data Management Management Data Lake Data Warehouse 52

Knowledge Hut

SEPTEMBER 26, 2023

Organisations are constantly looking for robust and effective platforms to manage and derive value from their data in the constantly changing landscape of data analytics and processing. These platforms provide strong capabilities for data processing, storage, and analytics, enabling companies to fully use their data assets.

Data Lake 52

Data Lake Database-centric Machine Learning Pipeline-centric 52

Data Engineering Weekly

MAY 5, 2024

link] LinkedIn: LakeChime - A Data Trigger Service for Modern Data Lakes LinkedIn points out two critical flaws in a partitioned approach to data management. The granularity of partition creation constrained data consumption. However, the Map and Array comes with its cost.

Data Engineer 68

Data Engineer Data Engineering Engineering Google Cloud 68

The Modern Data Company

MAY 8, 2023

Data lakes, data warehouses, data hubs, data lakehouses, and data operating systems are data management and storage solutions designed to meet different needs in data analytics, integration, and processing. This feature allows for a more flexible exploration of data.

Data Management 52

Data Management Management Data Lake Data Governance 52

The Modern Data Company

MAY 8, 2023

Data lakes, data warehouses, data hubs, data lakehouses, and data operating systems are data management and storage solutions designed to meet different needs in data analytics, integration, and processing. This feature allows for a more flexible exploration of data.

Data Management 52

Data Management Management Data Lake Data Governance 52

The Modern Data Company

MAY 8, 2023

Data lakes, data warehouses, data hubs, data lakehouses, and data operating systems are data management and storage solutions designed to meet different needs in data analytics, integration, and processing. This feature allows for a more flexible exploration of data.

Data Management 52

Data Management Management Data Lake Data Governance 52

AltexSoft

MAY 12, 2023

What is unstructured data? Definition and examples Unstructured data , in its simplest form, refers to any data that does not have a pre-defined structure or organization. It can come in different forms, such as text documents, emails, images, videos, social media posts, sensor data, etc.

Unstructured Data 59

Unstructured Data NoSQL Hadoop Data Lake 59

Cloudera

OCTOBER 7, 2021

Those decentralization efforts appeared under different monikers through time, e.g., data marts versus data warehousing implementations (a popular architectural debate in the era of structured data) then enterprise-wide data lakes versus smaller, typically BU-Specific, “data ponds”.

Architecture 124

Architecture Metadata Kafka Government 124

AltexSoft

MARCH 30, 2023

What is Databricks Databricks is an analytics platform with a unified set of tools for data engineering, data management , data science, and machine learning. It combines the best elements of a data warehouse, a centralized repository for structured data, and a data lake used to host large amounts of raw data.

Scala 64

Scala Data Lake Machine Learning BI 64

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

Ascend.io

AUGUST 31, 2023

Secondly , the rise of data lakes that catalyzed the transition from ELT to ELT and paved the way for niche paradigms such as Reverse ETL and Zero-ETL. Still, these methods have been overshadowed by EtLT — the predominant approach reshaping today’s data landscape.

Data Lake 52

Data Lake Data Warehouse ETL Tools Data Pipeline 52

LinkedIn Engineering

JULY 19, 2023

Open source data lakehouse deployments are built on the foundations of compute engines (like Apache Spark, Trino, Apache Flink), distributed storage (HDFS, cloud blob stores), and metadata catalogs / table formats (like Apache Iceberg, Delta, Hudi, Apache Hive Metastore). While functional, our current setup for managing tables is fragmented.

Big Data 78

Big Data Data Management Management Metadata 78

Snowflake

JULY 24, 2023

The company uses Snowflake’s Data Cloud to give business users quick and advanced data access using only basic SQL skills, meaning it has accelerated time-to-insight and decision-making. The company also uses external tables to directly access the semi-structured data within Snowflake.

Engineering 52

Engineering Building Finance Data Lake 52

AltexSoft

MARCH 14, 2023

As the volume and complexity of data continue to grow, organizations seek faster, more efficient, and cost-effective ways to manage and analyze data. In recent years, cloud-based data warehouses have revolutionized data processing with their advanced massively parallel processing (MPP) capabilities and SQL support.

IT 59

IT Data Warehouse Data Governance Data Lake 59

ProjectPro

DECEMBER 7, 2021

In broader terms, two types of data -- structured and unstructured data -- flow through a data pipeline. The structured data comprises data that can be saved and retrieved in a fixed format, like email addresses, locations, or phone numbers. What is a Big Data Pipeline?

Data Pipeline 52

Data Pipeline Architecture Kafka AWS 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

AltexSoft

AUGUST 22, 2022

A data fabric is an architecture design presented as an integration and orchestration layer built on top of multiple disjointed data sources like relational databases , data warehouses , data lakes, data marts , IoT , legacy systems, etc., to provide a unified view of all enterprise data.

Architecture 52

Architecture Metadata Data Lake Machine Learning 52

Striim

OCTOBER 11, 2024

While legacy ETL has a slow transformation step, modern ETL platforms, like Striim, have evolved to replace disk-based processing with in-memory processing. This advancement allows for real-time data transformation , enrichment, and analysis, providing faster and more efficient data processing.

Data Pipeline 52

Data Pipeline MongoDB Unstructured Data Data Lake 52

Edureka

FEBRUARY 7, 2023

One can use polybase: From Azure SQL Database or Azure Synapse Analytics, query data kept in Hadoop, Azure Blob Storage, or Azure Data Lake Store. It does away with the requirement to import data from an outside source. Export information to Azure Data Lake Store, Azure Blob Storage, or Hadoop.

Data Engineering 52

Data Engineering Data Engineer Engineering Hadoop 52

Edureka

JULY 4, 2024

Databricks runs on an optimized Spark version and gives you the option to select GPU-enabled clusters, making it more suitable for complex data processing. The platform’s massive parallel processing (MPP) architecture empowers you with high-performance querying of even massive datasets.

Data Lake 40

Data Lake Pipeline-centric Data Warehouse ETL Tools 40

Monte Carlo

MAY 28, 2024

This involves connecting to multiple data sources, using extract, transform, load ( ETL ) processes to standardize the data, and using orchestration tools to manage the flow of data so that it’s continuously and reliably imported – and readily available for analysis and decision-making.

Data Ingestion 52

Data Ingestion Architecture Designing Hadoop 52

Edureka

JULY 4, 2024

Choose Amazon S3 for cost-efficient storage to store and retrieve data from any cluster. It provides an efficient and flexible way to manage the large computing clusters that you need for data processing, balancing volume, cost, and the specific requirements of your big data initiative.

AWS 52

AWS Amazon Web Services Hadoop Big Data 52

Monte Carlo

JUNE 14, 2023

5 Data pipeline architecture designs and their evolution The Hadoop era , roughly 2011 to 2017, arguably ushered in big data processing capabilities to mainstream organizations. Data then, and even today for some organizations, was primarily hosted in on-premises databases with non-scalable storage.

Data Pipeline 52

Data Pipeline Architecture Data Lake Data Warehouse 52

Silectis

JANUARY 3, 2021

BI (Business Intelligence) Strategies and systems used by enterprises to conduct data analysis and make pertinent business decisions. Big Data Large volumes of structured or unstructured data. Big Query Google’s cloud data warehouse. Data Lake A storage repository where data is stored in its raw format.

Data Engineering 40

Data Engineering Data Engineer Engineering Hadoop 40

Cloudera

JANUARY 22, 2019

Today’s data landscape is characterized by exponentially increasing volumes of data, comprising a variety of structured, unstructured, and semi-structured data types originating from an expanding number of disparate data sources located on-premises, in the cloud, and at the edge. Data orchestration.

Big Data 44

Big Data NoSQL Hadoop Data Lake 44

Edureka

JUNE 1, 2023

It provides an advanced features to process and analyze the huge amount of data in a day to day world. Why Prefer Cloud for Data Analytics? Cloud technology can be used to build entire data lakes, data warehousing, and data analytics solutions. Why AWS Data Analytics?

AWS 52

AWS Data Analytics Cloud Amazon Web Services 52

AltexSoft

MAY 14, 2021

A single car connected to the Internet with a telematics device plugged in generates and transmits 25 gigabytes of data hourly at a near-constant velocity. 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. Data storage and processing.

Big Data 78

Big Data Data Analytics IT NoSQL 78

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

Striim

AUGUST 22, 2024

Streams of data are continuously queried with Streaming SQL , enabling correlation, anomaly detection, complex event processing, artificial intelligence/machine learning, and live visualization. Because of this, streaming analytics is especially impactful for fraud detection, log analysis, and sensor data processing use cases.

Data Warehouse 52

Data Warehouse Retail Machine Learning Database 52

ProjectPro

JANUARY 19, 2022

Data engineering is a new and ever-evolving field that can withstand the test of time and computing developments. Companies frequently hire certified Azure Data Engineers to convert unstructured data into useful, structured data that data analysts and data scientists can use.

Data Engineering 52

Data Engineering Data Engineer Engineering Data Storage 52