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Data Sources, Databases, ETL Tools

Navigating the Data Landscape:

Exploring Data Sources, Databases, and ETL Tools for Machine Learning Projects

Introduction

Data sources: Data sources refer to the origins or locations from which data is collected or generated. They can include various platforms, systems, devices, or applications that generate or store data, such as databases, APIs, files, sensors, social media platforms, or web services.

Databases: Databases are organized collections of structured data that are stored, managed, and accessed using database management systems (DBMS). They provide a structured way to store and retrieve data efficiently, enabling data storage, retrieval, manipulation, and querying operations for various applications.

ETL tools: ETL stands for Extract, Transform, Load. ETL tools are software applications or platforms designed to facilitate the extraction, transformation, and loading of data from multiple sources into a target destination, such as a data warehouse or database. These tools help automate and streamline the process of collecting data from diverse sources, performing data transformations or cleansing, and loading the processed data into a centralized storage or analytics platform.

Machine learning projects require various types of data, such as text, image/video, tabular, or voice/music. These data may be divided into timeseries or non-timeseries data, as well as stored, live/stream, or real-time data depending on liveness. Volume may range from a few megabytes to several petabytes/exabytes per day, depending on the data’s source. Managing such varied data types, volumes, and liveness requires different technologies for storage, access, transmission, processing, and analysis, of which hundreds are available.

Extracting data from a range of prototypes, technologies, and security systems is difficult due to the differing connectors, authentications, and authorizations required. This article aims to present various data format/data storage/data management technologies that can be applied in a data science project, which can include databases, data sources, and ETL tools. It is unlikely that any single project would require all these systems/technologies, but it is essential to have an overview of the available technologies and their complexity of data processing, storage, transmission, and analysis, particularly when dealing with multiple technologies simultaneously.

Finally, a list of over 200+ data sources, databases, and ETL tools is provided, each with distinctive features for handling specific data types, scale, security, and performance requirements.

List of Data Technologies

Sno Name Category
1 Act CRM CRM & ERP
2 Active Directory COLLABORATION
3 Acumatica CRM & ERP
4 Adobe Analytics MARKETING
5 ADP ACCOUNTING
6 Airtable COLLABORATION
7 Alfresco COLLABORATION
8 Amazon Athena BIG DATA & NOSQL
9 Amazon Aurora RDBMS
10 Amazon DynamoDB BIG DATA & NOSQL
11 Amazon Marketplace E-COMMERCE
12 Amazon RDS RDBMS
13 Amazon Redshift BIG DATA & NOSQL
14 Amazon S3 FILE & API
15 Apache Avro FILE & API
16 Apache Cassandra BIG DATA & NOSQL
17 Apache H Base BIG DATA & NOSQL
18 Apache Hive BIG DATA & NOSQL
19 Apache Impala RDBMS
20 Asana COLLABORATION
21 Authorize.Net E-COMMERCE
22 Autify COLLABORATION
23 Avalara Avatax ACCOUNTING
24 AWS Management COLLABORATION
25 Azure Analysis Services RDBMS
26 Azure Cosmos DB BIG DATA & NOSQL
27 Azure Data Catalog BIG DATA & NOSQL
28 Azure Data Lake Storage BIG DATA & NOSQL
29 Azure Management COLLABORATION
30 Azure Synapse RDBMS
31 Basecamp COLLABORATION
32 Big Commerce E-COMMERCE
33 Blackbaud ACCOUNTING
34 Box FILE & API
35 Bugzilla COLLABORATION
36 Bullhorn CRM CRM & ERP
37 Casandra Non Relational Data Storage
38 CockroachDB BIG DATA & NOSQL
39 Confluence COLLABORATION
40 Couchbase BIG DATA & NOSQL
41 CSV FILE & API
42 Databricks BIG DATA & NOSQL
43 DataRobot COLLABORATION
44 DBVisualizer Relational Data Storage
45 Digital Ocean FILE & API
46 DocuSign COLLABORATION
47 Dropbox FILE & API
48 Dynamics 365 FinOps CRM & ERP
49 Dynamics Business Central CRM & ERP
50 Dynamics GP ACCOUNTING
51 Dynamics Nav ACCOUNTING
52 eBay E-COMMERCE
53 Edgar Online E-COMMERCE
54 ElasticSearch BIG DATA & NOSQL
55 Email COLLABORATION
56 EnterpriseDB Relational Data Storage
57 EnterpriseDB RDBMS
58 Epicor ERP CRM & ERP
59 ETL Greenplum RDBMS
60 Evernote COLLABORATION
61 Exact Online CRM & ERP
62 Facebook Ads MARKETING
63 FedEx E-COMMERCE
64 Financial Force CRM & ERP
65 Freshbooks ACCOUNTING
66 Freshdesk ACCOUNTING
67 Github COLLABORATION
68 Gmail COLLABORATION
69 Google Ads MARKETING
70 Google Analytics MARKETING
71 Google BigQuery BIG DATA & NOSQL
72 Google Calendar COLLABORATION
73 Google Cloud Storage FILE & API
74 Google Contacts COLLABORATION
75 Google Data Catalog BIG DATA & NOSQL
76 Google Dataset  
77 Google Drive FILE & API
78 Google Sheets COLLABORATION
79 Google Spanner BIG DATA & NOSQL
80 GraphQL BIG DATA & NOSQL
81 Harper DB BIG DATA & NOSQL
82 HDFS FILE & API
83 Highrise CRM & ERP
84 HPCC Systems BIG DATA & NOSQL
85 HubSpot MARKETING
86 IBM Cloud Objectz BIG DATA & NOSQL
87 IBM Cloud SQL Query FILE & API
88 IBM Cloudant BIG DATA & NOSQL
89 IBM Db2 RDBMS
90 Instagram Ads MARKETING
91 JDBC-ODBC Bridge RDBMS
92 Jira by Atlassian COLLABORATION
93 Jira Service Desk COLLABORATION
94 JSON FILE & API
95 Kintone COLLABORATION
96 LDAP FILE & API
97 LinkedIn Ads MARKETING
98 Log Files from OS FILE & API
99 Magento E-COMMERCE
100 MailChimp MARKETING
101 MariaDB RDBMS
102 Marketo MARKETING
103 MarkLogic BIG DATA & NOSQL
104 Microsoft Ads MARKETING
105 Microsoft Dynamics 365 Sales CRM & ERP
106 Microsoft Excel FILE & API
107 Microsoft SQL Server RDBMS
108 Microsoft Teams COLLABORATION
109 MongoDB BIG DATA & NOSQL
110 MongoDB Atlas BIG DATA & NOSQL
111 MS Access RDBMS
112 MS CDS FILE & API
113 MS Exchange Connector COLLABORATION
114 MS OneDrive FILE & API
115 MS OneNote COLLABORATION
116 MS Planner COLLABORATION
117 MS Project COLLABORATION
118 MYOB ACCOUNTING
119 MySQL RDBMS
120 Neo4J Non Relational Data Storage
121 NetSuite CRM & ERP
122 OData FILE & API
123 Odoo CRM & ERP
124 Open Exchange Rates E-COMMERCE
125 Oracle RDBMS
126 Oracle DB Relational Data Storage
127 Oracle Eloqua MARKETING
128 Oracle Sales Cloud MARKETING
129 Parquet FILE & API
130 Paypal ACCOUNTING
131 PDF FILE & API
132 Pinterest MARKETING
133 PostgreSQL RDBMS
134 Presto BIG DATA & NOSQL
135 Presto DB BIG DATA & NOSQL
136 Quandl E-COMMERCE
137 Quickbase COLLABORATION
138 QuickBooks Online ACCOUNTING
139 Reckon ACCOUNTING
140 Redis BIG DATA & NOSQL
141 RedisDB Non Relational Data Storage
142 REST FILE & API
143 RSS FILE & API
144 Sage 300 CRM & ERP
145 Sage ACCOUNTING
146 Salesforce CRM & ERP
147 Salesforce Chatter MARKETING
148 SAP Business One DI CRM & ERP
149 SAP Business One RDBMS
150 SAP BusinessObjects BI COLLABORATION
151 SAP ByDesign CRM & ERP
152 SAP Concur ACCOUNTING
153 SAP ERP CRM & ERP
154 SAP Fieldglass E-COMMERCE
155 SAP HANA RDBMS
156 SAP HANA XS Advanced RDBMS
157 SAP Hybris c4c RDBMS
158 SAP Netweaver CRM & ERP
159 SAP Success Factors COLLABORATION
160 SAS Datasets BIG DATA & NOSQL
161 SAS xpt FILE & API
162 SendGrid MARKETING
163 ServiceNow CRM & ERP
164 SFTP FILE & API
165 SharePoint COLLABORATION
166 ShipStation E-COMMERCE
167 Shopify E-COMMERCE
168 Slack COLLABORATION
169 Smartsheet COLLABORATION
170 Snowflake BIG DATA & NOSQL
171 Splunk MARKETING
172 SQL Analysis Services RDBMS
173 Square E-COMMERCE
174 Streak CRM & ERP
175 Sugar CRM CRM & ERP
176 Suite CRM CRM & ERP
177 SurveyMonkey MARKETING
178 Sybase IQ RDBMS
179 Sybase RDBMS
180 Tally CRM & ERP
181 TaxJar ACCOUNTING
182 Teradata RDBMS
183 Trello COLLABORATION
184 Trino BIG DATA & NOSQL
185 Tsheets ACCOUNTING
186 TSV FILE & API
187 Twilio FILE & API
188 TXT FILE & API
189 UPS E-COMMERCE
190 USPS E-COMMERCE
191 Veeva CRM & ERP
192 Wasabi FILE & API
193 WordPress COLLABORATION
194 Workday ACCOUNTING
195 X-Cart E-COMMERCE
196 xBase RDBMS
197 Xero ACCOUNTING
198 Xero Workflow Max COLLABORATION
199 XML FILE & API
200 YouTube Analytics MARKETING
201 Zendesk COLLABORATION
202 Zip Files FILE & API
203 Zoho Books ACCOUNTING
204 Zoho CRM CRM & ERP

Conclusion:

In the ever-expanding landscape of data-driven technologies, understanding and harnessing the power of data sources, databases, and ETL tools are crucial for successful machine learning projects. This article has provided a good summary list for data science.

We delved into the concept of data sources, highlighting their diverse nature and the wide array of platforms, systems, and applications that contribute to the data ecosystem. Recognizing the origins and types of data is essential for sourcing relevant and reliable datasets that drive machine learning models forward.

Additionally, we examined the significance of ETL tools, which streamline the extraction, transformation, and loading of data from multiple sources into centralized destinations. These tools automate the data integration process, ensuring that valuable insights can be derived from diverse and complex datasets.

Machine learning projects demand a careful consideration of data types, volumes, liveness, and technological requirements. By understanding the available data storage, management, and processing technologies, data scientists can make informed decisions that align with project objectives and ensure optimal performance.

To aid readers in their data science endeavors, we provided a comprehensive list of over 200+ data sources, databases, and ETL tools. Each entry display the category of technology.

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