You can check 

http://tocs.ulb.tu-darmstadt.de/182444961.pdf

http://www2.sas.com/proceedings/sugi30/203-30.pdf

http://www.stat-help.com/factor.pdf

There many others as well.

Theo Dijkstra answer is comprehensive so I will just conclude briefly. If Your main aim is only to reduce observed data - use PCA.  If Your aim is to reason about latent factors - use factor analysis.

In "rice and beans", that is a saying in Puerto Rico to explain something complicated in a simple way. PCA and factor analysis has different aims. In the PCA usually you are looking to reduce the quantity of variables, let say you have 7 variables that may be important to consider for a regression model but have a small sample. If those variables are somehow related you could create an index or composite that consider each of them but giving a weight according to their significance for the composite. Some people also use PCA to reduce number of items in a scale, let say from 36 to 12, according to results.

In factor analysis you are not necessarily looking for variable reduction, although at the end you could end up with less variables integrated into sub-scales or dimensions. In factor analysis you are looking for exploring a factor structure in a scale or confirming the factor structure based on literature or previous studies. Right now I am developing a masculine depression scale which , based on the literature, should have items that responds to 6 subdimensions. The general construct is masculine depression  measure with 63 items. Running an exploratory factor analysis I may end up with less items, but also identifying the six sub-scales or dimensions previously identified. 

It is important to consider that although outputs from both may look similar, actually the statistical procedures are different precisely since the aims are different. In my experience, although sometimes results are quite similar in other, results could be extremely different. I suggest to be sure what is what you want to do in order to be sure you are using the one that responds to your aim.  Larry Hatcher in a SAS book for factor analysis and structural equation modeling give a very simple explanation and distintion.

I am a clinical psychologist who happens to like statistics a little more.

Factor analysis (FA) is a group of statistical methods used to understand and simplify patterns of relationships underlying measured variables (Beavers, Lounsbury, Richards, Huck, Skolits, & Esquivel, 2013; Fabrigar, Wegener, MacCallum, & Strahan, 1999; Schmitt, 2011). Factor analysis is a concept that includes both exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) (Jennrich & Bentler, 2011).

CFA tests whether a known factor model can predict a set of observed data (DeCoster, 1998). Researchers use CFA to verify or confirm hypotheses or theory (Ruscio & Roche, 2012; Schmitt, 2011), establish the validity of the factor model, compare two models using the same data, test the significance of factor loading, test relationships between factor loadings, test for correlation or lack of correlation of factors, and assess convergent and discriminate validity of measures (DeCoster, 1998).

EFA tests the number of common factors that influence measures and tests the strength and relationship between each common factor to the corresponding measure (DeCoster, 1998). Researchers use EFA to identify the nature of constructs that underlie responses given in a questionnaire, determine sets of items that interconnect, demonstrate the depth and breadth of measurement scales, classify the most important features of a group of items, and generate factor scores that represent the underlying constructs (DeCoster, 1998). Because EFA is a multivariate statistical approach, it is appropriate for reducing the number of factors, examining relationships between categories, and evaluating the construct validity of a measurement scale (Williams et al., 2010).

Exploratory factor analysis involves a series of statistical analysis steps. The first is the planning phase, where it is determined if the data is suitable for EFA by selecting the sample size then after collecting the data, creating a correlation matrix and testing for adequacy. The second step is to extract factors. The third step is to determine the number of factors to retain. The fourth step is factor rotation. The fifth step is to interpret the factor structure.

Principal component analysis (PCA) is a method of factor extraction (the second step mentioned above). Researchers use PCA when they want to reduce the number of variables while retaining as much of the original variance as possible (Conway & Huffcutt, 2003).

REFERNCES

Beavers, A. S., Lounsbury, J. W., Richards, J. K., Huck, S. W., Skolits, G J., & Esquivel, S. L. (2013). Practical considerations for using exploratory factor analysis in educational research. Practical Assessment, Research & Evaluation, 18(6), 1-13. Retrieved from http://www.pareonline.net/pdf/v18n6.pdf

Conway, J. M., & Huffcutt, A. I. (2003). A review and evaluation of exploratory factor analysis practices in organizational research. Organizational Research Methods, 6, 147-168. doi:10.1177/1094428103251541

DeCoster, J. (1998). Overview of Factor Analysis. Retrieved from http://www.stat-help.com/factor.pdf

Fabrigar, L. R., Wegener, D. T., MacCallum, R. C. & Strahan, E J. (1999). Evaluating the use of exploratory factor analysis in psychological research. Psychological Methods, 4, 272-299. doi:1082-989X/99/S3.00

Jennrich, R. I., & Bentler, P. M. (2011). Exploratory bi-factor analysis. Psychometrika, 76, 537-549. foi:10.1007/s11336-011-9218-4

Ruscio, J., & Roche, B. (2012). Determining the number of factors to retain in exploratory factor analysis using comparison data of known factorial structure. Psychologocial Assessment, 24(2), 282-292. doi:10.1037/a0025697

Schmitt, T. A. (2011). Current methodological considerations in exploratory and confirmatory factor analysis. Journal of Psychoeducational Assessment, 29(4), 304-321. doi:10.1177/0734282911406653

Floyd and Widaman's 1995 Psych Assessment paper on factor analysis reviews the Monte Carlo studies comparing these two extraction techniques. Under optimal conditions (specifically, in samples of at least 300 respondents, in analyses of at least 30 items, for factors represented by at least 10 items, and for factors containing items with high communalities / loadings) these two approaches give completely convergent results. However, if one or more of these conditions is not met, then the Monte Carlo results suggested that EFA (or what's called principle axis factoring in SPSS) gave more appropriate results than PCA extraction. As a result of those Monte Carlo studies, I found reviewers tend to dislike seeing PCA at all (even under optimal conditions). Consequently, I have shifted to exclusively using EFA whenever possible.

Hope that helps!!

please check here

Pallant, J. (2005). SPSS survival manual: A step by step guide to using SPSS for

windows (version 12). New South Wales, Australia: Allen & Unwin.

the two techniques are very similar in that they fall within the so-called generalized linear model. I suggest you consult the book that deals with this subject

http://www.francoangeli.it/Ricerca/Scheda_libro.aspx?ID=21599&Tipo=Libro&titolo=Factor+analysis+and+principal+component+analysis++

Pallant, J. (2005). SPSS survival manual: A step by step guide to using SPSS for

windows (version 12). New South Wales, Australia: Allen & Unwin.

A little about the PCA in the article: https://www.researchgate.net/publication/319469038_New_Interpretation_of_Principal_Components_Analysis

Article New Interpretation of Principal Components Analysis

Run exploratory factor analysis (EFA) if you assume or wish to test a theoretical model of latent factors causing observed variables. EFA identifies the number of latent constructs and the underlying factor structure of a set of variables.....

Run principal component analysis (PCA) if you want to simply reduce your correlated observed variables to a smaller set of important independent composite variables. PCA reduces the number of observed variables to a smaller number of principal components which account for most of the variance of the observed variables

In addition to a comprehensive overview, I think the following links will be useful for you.

https://www.google.com/amp/s/www.researchgate.net/post/Factor_analysis_and_principal_component_analysis/amp

https://www.palass.org/publications/newsletter/palaeomath-101/palaeomath-part-6-factor-analysis

My understanding is that FA is a measurement model of a latent variable, while PCA is a linear combination of variables in order to create one or more index variables from a larger set of measured variables, which is named as components.