Analysts Corner

JUNE 18, 2023

ETL Developer: Defining the Role An ETL developer is a professional responsible for designing, implementing, and managing ETL processes that extract, transform, and load data from various sources into a target data store, such as a data warehouse. Oracle, SQL Server, MySQL) Experience with ETL tools and technologies (e.g.,

Data Warehouse 130

Data Warehouse Data Quality Documentation Data Governance 130

Sisense

JULY 22, 2020

They hold structured data from relational databases (rows and columns), semi-structured data ( CSV , logs, XML , JSON ), unstructured data (emails, documents, PDFs), and binary data (images, audio , video). Sisense provides instant access to your cloud data warehouses. Connect tables.

Digital Transformation 98

Digital Transformation Data Warehouse IBM Support IBM cost 98

Sisense

JUNE 26, 2019

It was a big investment that is still viable, and one that forms the legacy databases of many companies today. . Fast forward to Big Data. Now we can throw in the four 4 V’s of Big Data (Variety, Volume, Velocity, and Veracity) and compound the data issues of the enterprise with an even bigger data issue.

Data Warehouse 75

Data Warehouse Data Modelling Big Data Monitoring 75

Sisense

APRIL 7, 2020

Dealing with Data is your window into the ways Data Teams are tackling the challenges of this new world to help their companies and their customers thrive. We live in an era of Big Data. The sheer amount of data being generated is greater than ever (we hit 18 zettabytes in 2018) and will continue to grow.

Big Data 86

Big Data Data Warehouse Product Management Product Management 86

Sisense

JANUARY 15, 2021

This is one of the reasons we’ve seen the rise of data teams — they’ve grown beyond Silicon Valley startups and are finding homes in Fortune 500 companies. As data has become more massive, the technical skills needed to wrangle it have also increased. Situation #2: Established company creates a data team for deeper insights.

Skillset 75

Skillset Data Warehouse Data Modelling Business Intelligence 75

Astera

NOVEMBER 28, 2022

Data warehouses have long served as a single source of truth for data-driven companies. But as data complexity and volumes increase, it’s time to look beyond the traditional data ecosystems. Does that mean it’s the end of data warehousing? Does that mean it’s the end of data warehousing?

Data Warehouse 52

Data Warehouse Data Modelling Data Management Big Data 52

Astera

MAY 25, 2023

Unlocking the Potential of Amazon Redshift Amazon Redshift is a powerful cloud-based data warehouse that enables quick and efficient processing and analysis of big data. Amazon Redshift can handle large volumes of data without sacrificing performance or scalability. What Is Amazon Redshift? 

Data Warehouse 52

Data Warehouse Data Modelling Banking Real-time Data 52

Sisense

FEBRUARY 24, 2021

Data space dimension: Traditional data vs. big data. This dimension focuses on what type of data the CDO has to wrangle. Traditional datasets are often relational data found at the core of transactional services and operations: Think of an accounting system or point-of-sale system that spans multiple locations.

Chief Data Officer 69

Chief Data Officer Data Warehouse Big Data Innovation 69

Cprime

MAY 4, 2023

Key Data Integration Use Cases Let’s focus on the four primary use cases that require various data integration techniques: Data ingestion Data replication Data warehouse automation Big data integration Data Ingestion The data ingestion process involves moving data from a variety of sources to a storage location such as a data warehouse or data lake.

Data Warehouse 52

Data Warehouse Big Data Finance Data Quality 52

Astera

NOVEMBER 30, 2023

With rising data volumes, dynamic modeling requirements, and the need for improved operational efficiency, enterprises must equip themselves with smart solutions for efficient data management and analysis. This is where Data Vault 2.0 It supersedes Data Vault 1.0, What is Data Vault 2.0? Data Vault 2.0

Data Warehouse 52

Data Warehouse Big Data Healthcare Data Governance 52

Astera

APRIL 27, 2023

The modern data stack (MDS) is a collection of tools for data integration that enable organizations to collect, process, store and analyze data. Being based on a well-integrated cloud platform, modern data stack offers scalability, efficiency, and proficiency in data handling.

Data Warehouse 52

Data Warehouse Big Data Data Analytics Data Analytics 52

Astera

JULY 3, 2024

These data architectures include: Data Warehouse: A data warehouse is a central repository that consolidates data from multiple sources into a single, structured schema. It organizes data for efficient querying and supports large-scale analytics.

Data Architecture 40

Data Architecture Data Warehouse Data Governance Government 40

Astera

MAY 31, 2024

Data integration combines data from many sources into a unified view. It involves data cleaning, transformation, and loading to convert the raw data into a proper state. The integrated data is then stored in a Data Warehouse or a Data Lake. Data warehouses and data lakes play a key role here.

Data Analytics 52

Data Analytics Data Analytics Data Warehouse Healthcare 52

Sisense

JULY 14, 2019

If you just felt your heartbeat quicken thinking about all the data your company produces, ingests, and connects to every day, then you won’t like this next one: What are you doing to keep that data safe? Data security is one of the defining issues of the age of AI and Big Data. Understanding Your Users.

Data Security 75

Data Security Data Modelling Documentation Cybersecurity 75

Astera

MARCH 7, 2024

ETL testing is a set of procedures used to evaluate and validate the data integration process in a data warehouse environment. In other words, it’s a way to verify that the data from your source systems is extracted, transformed, and loaded into the target storage as required by your business rules.

Data Warehouse 52

Data Warehouse Data Quality Documentation Data Modelling 52

Astera

JANUARY 2, 2024

Free Download Here’s what the data management process generally looks like: Gathering Data: The process begins with the collection of raw data from various sources. Once collected, the data needs a home, so it’s stored in databases, data warehouses , or other storage systems, ensuring it’s easily accessible when needed.

Data Management 52

Data Management Data Warehouse Data Governance Data Quality 52

Sisense

MAY 27, 2019

Often with a background in advanced mathematics and/or statistical analysis, data scientists conduct high-level market and business research to help identify trends and opportunities, and then, to summarize, these findings are presented by the business analyst to the business and stakeholders in a manner that aids decision-making.

Skillset 75

Skillset Cloud Computing Visualization Big Data 75

Actian

MAY 21, 2019

The refinement process starts with the ingestion and aggregation of data from each of the source systems. This is often done in some sort of data warehouse. Once the data is in a common place, it must be merged and reconciled into a common data model – addressing, for example, duplication, gaps, time differences and conflicts.

Big Data 40

Big Data Data Warehouse Artificial Intelligence Digital Transformation 40

The BAWorld

SEPTEMBER 2, 2021

Non-technical users can also work easily with structured data. Structured Data Example. can be grouped in a data warehouse for marketing analysis. This is a classic example of structured data and can be efficiently managed through a database. Unstructured Data. of organizations are investing in big data.

Big Data 52

Big Data Data Visualization Data Warehouse Visualization 52

Astera

MAY 30, 2024

One MIT Sloan Review research revealed extensive data analytics helps organizations provide individualized recommendations, fostering loyal customer relationships. What Is Big Data Analytics? Velocity : The speed at which this data is generated and processed to meet demands is exceptionally high.

Data Analytics 52

Data Analytics Data Analytics Data Mining Supply Chain 52

Astera

SEPTEMBER 20, 2024

his setup allows users to access and manage their data remotely, using a range of tools and applications provided by the cloud service. Cloud databases come in various forms, including relational databases, NoSQL databases, and data warehouses. There are several types of NoSQL databases, including document stores (e.g.,

Data Warehouse 40

Data Warehouse Cloud Computing E-Commerce Documentation 40

Data Pine

SEPTEMBER 29, 2022

With quality data at their disposal, organizations can form data warehouses for the purposes of examining trends and establishing future-facing strategies. Industry-wide, the positive ROI on quality data is well understood. Business/Data Analyst: The business analyst is all about the “meat and potatoes” of the business.

Data Quality 96

Data Quality Monitoring Big Data Government 96

Data Pine

MARCH 31, 2022

This could involve anything from learning SQL to buying some textbooks on data warehouses. A data scientist has a similar role as the BI analyst, however, they do different things. While analysts focus on historical data to understand current business performance, scientists focus more on data modeling and prescriptive analysis.

Business Intelligence 95

Business Intelligence Business Intelligence Data Visualization Visualization 95

Astera

JUNE 3, 2024

These databases are ideal for big data applications, real-time web applications, and distributed systems. Hierarchical databases The hierarchical database model organizes data in a tree-like structure with parent-child relationships. Data volume and growth: Consider the current data size and anticipated growth.

Data Warehouse 52

Data Warehouse Documentation E-Commerce Data Management 52

Astera

AUGUST 22, 2023

Data Warehousing AI Select: This feature aids you in identifying potential Fact and Dimension tables from selected entities. By leveraging AI capabilities, it automatically determines the appropriate classification, streamlining the data modeling process for entities with uncertain categorization.

Innovation 52

Innovation Documentation Data Analytics Data Analytics 52

Astera

AUGUST 28, 2024

The concept of data analysis is as old as the data itself. Big data and the need for quickly analyzing large amounts of data have led to the development of various tools and platforms with a long list of features. While it offers a graphical UI, data modeling is still complex for non-technical users.

IBM Support 40

IBM Support IBM Maintenance IBM cost Data Mining 40

Insight Software

MAY 1, 2023

These sit on top of data warehouses that are strictly governed by IT departments. The role of traditional BI platforms is to collect data from various business systems. It is organized to create a top-down model that is used for analysis and reporting. Ideally, your primary data source should belong in this group.

Embedded Analytics 52

Embedded Analytics IBM Support IBM Maintenance IBM cost 52

Insight Software

NOVEMBER 21, 2023

To have any hope of generating value from growing data sets, enterprise organizations must turn to the latest technology. You’ve heard of data warehouses, and probable data lakes, but now, the data lakehouse is emerging as the new corporate buzzword. To address this, the data lakehouse was born.

IBM Maintenance 52

IBM Maintenance Data Warehouse IBM cost Supply Chain 52