Navigating the Data Landscape: Exploring Data Sources, Databases, and ETL Tools for Machine Learning Projects

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.